Temat: swarm algorithm - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Simulation on vessel intelligent collision avoidance based on artificial fish swarm algorithm
Autorzy:: Li, W.
Ma, W.
Powiązania:: https://bibliotekanauki.pl/articles/260153.pdf
Data publikacji:: 2016
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: simulation
collision avoidance
artificial fish swarm algorithm
Opis:: TAs the rapid development of the ship equipments and navigation technology, vessel intelligent collision avoidance theory was researched world widely. Meantime, more and more ship intelligent collision avoidance products are put into use. It not only makes the ship much safer, but also lighten the officers work intensity and improve the ship’s economy. The paper based on the International Regulation for Preventing Collision at sea and ship domain theories, with the ship proceeding distance when collision avoidance as the objective function, through the artificial fish swarm algorithm to optimize the collision avoidance path, and finally simulates overtaking situation, crossing situation and head-on situation three classic meeting situation of ships on the sea by VC++ computer language. Calculation and simulation results are basically consistent with the actual situation which certifies that its validity.
Źródło:: Polish Maritime Research; 2016, S 1; 138-143
1233-2585
Pojawia się w:: Polish Maritime Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Selection of the heat transfer coefficient using swarming algorithms
Autorzy:: Gawrońska, Elżbieta
Dyja, Robert
Zych, Maria
Domek, Grzegorz
Powiązania:: https://bibliotekanauki.pl/articles/2204686.pdf
Data publikacji:: 2022
Wydawca:: Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:: swarm algorithm
ABC algorithm
ACO algorithm
heat transfer coefficient
computer simulation
numerical modelling
Opis:: The article presents the use of swarming algorithms in selecting the heat transfer coefficient, taking into account the boundary condition of the IV types. Numerical calculations were made using the proprietary TalyFEM program and classic form of swarming algorithms. A function was also used for the calculations, which, during the calculation, determined the error of the approximate solution and was minimalised using a pair of individually employed algorithms, namely artificial bee colony (ABC) and ant colony optimisation (ACO). The tests were carried out to select the heat transfer coefficient from one range. Describing the geometry for a mesh of 408 fine elements with 214 nodes, the research carried out presents two squares (one on top of the other) separated by a heat transfer layer with a κ coefficient. A type III boundary condition was established on the right and left of both edges. The upper and lower edges were isolated, and a type IV boundary condition with imperfect contact was established between the squares. Calculations were made for ABC and ACO, respectively, for populations equal to 20, 40 and 60 individuals and 2, 6 and 12 iterations. In addition, in each case, 0%, 1%, 2% and 5% noise of the reference values were also considered. The obtained results are satisfactory and very close to the reference values of the κ parameter. The obtained results demonstrate the possibility of using artificial intelligence (AI) algorithms to reconstruct the IV type boundary condition value during heat conduction modelling.
Źródło:: Acta Mechanica et Automatica; 2022, 16, 4; 325--339
1898-4088
2300-5319
Pojawia się w:: Acta Mechanica et Automatica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Optimization of electric and magnetic field intensities in proximity of power lines using genetic and particle swarm algorithms
Autorzy:: Król, K.
Machczyński, W.
Powiązania:: https://bibliotekanauki.pl/articles/141588.pdf
Data publikacji:: 2018
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: power line
electric field
magnetic field
optimization
genetic algorithm
particle swarm algorithm
Opis:: The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot–Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single – circuit power line. A comparison of the used optimization algorithms was made.
Źródło:: Archives of Electrical Engineering; 2018, 67, 4; 829-843
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Design of a Superconducting Antenna Integrated with a Diplexer for Radio-Astronomy Applications
Autorzy:: Donelli, M.
Febvre, P.
Powiązania:: https://bibliotekanauki.pl/articles/309365.pdf
Data publikacji:: 2014
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: diplexer
microwave antenna
optimization techniques
particle swarm algorithm
radio astronomy
Opis:: This paper presents the design of a compact frontend diplexer for radio-astronomy applications based on a self complementary Bow-tie antenna, a 3 dB T-junction splitter and two pass-band fractal lters. The whole diplexer structure has been optimized by using an evolutionary algorithm. In particular the problem of the diplexer design is recast into an optimization one by dening a suitable cost function which is then minimized by mean of an evolutionary algorithm namely the Particle Swarm Optimization (PSO). An X band diplexer prototype was fabricated and assessed demonstrating a good agreement between numerical and experimental results.
Źródło:: Journal of Telecommunications and Information Technology; 2014, 3; 113-118
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Constrained optimization of the brushless DC motor using the salp swarm algorithm
Autorzy:: Knypiński, Łukasz
Devarepalli, Ramesh
Le Menach, Yvonnick
Powiązania:: https://bibliotekanauki.pl/articles/2135734.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: brushless DC motor
constrained optimization
finite element analysis
salp swarm algorithm
Opis:: This paper presents an algorithm and optimization procedure for the optimization of the outer rotor structure of the brushless DC (BLDC) motor. The optimization software was developed in the Delphi Tiburón development environment. The optimization procedure is based on the salp swarm algorithm. The effectiveness of the developed optimization procedurewas compared with genetic algorithm and particle swarmoptimization algorithm. The mathematical model of the device includes the electromagnetic field equations taking into account the non-linearity of the ferromagnetic material, equations of external supply circuits and equations of mechanical motion. The external penalty function was introduced into the optimization algorithm to take into account the non-linear constraint function.
Źródło:: Archives of Electrical Engineering; 2022, 71, 3; 775--787
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
Autorzy:: Arif, Salah
Djellal, Adel
Djebbari, Nawel
Belhaoues, Saber
Touati, Hassen
Guellati, Fatma Zohra
Bensouilah, Mourad
Powiązania:: https://bibliotekanauki.pl/articles/2174666.pdf
Data publikacji:: 2023
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: microcystis cell density
Multiple Linear Regression
Support Vector Machine
Particle Swarm Optimization
Genetic Algorithm
Bird Swarm Algorithm
Opis:: Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
Źródło:: Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
1898-1135
Pojawia się w:: Geomatics and Environmental Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Swarm Behaviour Optimisation Methods Based on an Original Algorithm
Metoda optymalizacji zachowania się roju na podstawie autorskiego algorytmu
Autorzy:: Falkowski, Krzysztof
Duda, Michał
Powiązania:: https://bibliotekanauki.pl/articles/2036929.pdf
Data publikacji:: 2021
Wydawca:: Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:: swarms
swarm algorithm
sweep coverage
coverage task
optimisation
optymalizacja
rój
algorytm roju
Opis:: This article presents an authorial swarm algorithm that performs coverage tasks using the Sweep Coverage method. The presented solution assumes stochastic movement of the objects in the swarm which allows them to be simple ones. Our goal was to find an optimal number of objects in the swarm. The main evaluated factors are time and energy consumption. Changing input data allowed us to designate different cases and to examine the influence of varying parameters of a single boid on a whole swarm behaviour.
W artykule przedstawiono metody znalezienia optymalnej wielkości roju dla danego zadania. Głównymi ocenianymi czynnikami są czas i zużycie energii. Autorskie rozwiązanie algorytmiczne pozwoliło na wyznaczenie różnych przypadków i zbadanie wpływu różnych parametrów pojedynczego boida na zachowanie całego roju. Obliczenie efektywności energetycznej pozwoliło na wyznaczenie dodatkowych informacji o optymalizacji liczby boidów w roju. Wyniki pokazują, że można ocenić najlepsze rozwiązania dla określonych założeń. Można znaleźć, jaka liczba boidów wykonałaby zadanie w jak najkrótszym czasie przy założonej energooszczędności. Można również znaleźć grupę z najlepszym czasem do uzyskania wskaźnika efektywności energetycznej, która wykonałaby zadanie przy najlepszej kombinacji najkrótszego czasu i zużytej energii. Dodatkowe testy ze zmieniającymi się zmiennymi pozwoliły określić ich wpływ na wynik. Wykazano, że prędkość i bezpieczna odległość są ze sobą połączone, ale zmiana prędkości jest bardziej znacząca dla mniejszych rojów, gdy zmiana bezpiecznej odległości ma większy wpływ na liczniejsze grupy. Wynika z tego, że dla małych grup lepsze są szybsze boidy, a dla liczniejszych rojów bardziej przydatne byłyby boidy, które mogą poruszać się bliżej. Zmienianie promienia obszaru skanowanego na każdym kroku wpływa na ogólną wydajność, ale prawie nie ma wpływu na efektywność energetyczną roju.
Źródło:: Problemy Mechatroniki : uzbrojenie, lotnictwo, inżynieria bezpieczeństwa; 2021, 12, 3 (45); 53-70
2081-5891
Pojawia się w:: Problemy Mechatroniki : uzbrojenie, lotnictwo, inżynieria bezpieczeństwa
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Koncepcja środowiska symulacyjnego do oceny samoorganizacji trasowania w sieci sensorycznej
The concept of the simulation environment designed to evaluate the self-organizing process of the sensors network routing
Autorzy:: Stankiewicz, K.
Powiązania:: https://bibliotekanauki.pl/articles/199141.pdf
Data publikacji:: 2015
Wydawca:: Instytut Techniki Górniczej KOMAG
Tematy:: trasowanie
samoorganizacja
algorytm roju
sieć sensoryczna
routing
self-organization
swarm algorithm
sensors networks
Opis:: Techniki Internetu Rzeczy (IoT – Internet of Things) oraz komunikacji bezpośredniej Maszyna do Maszyny (M2M - Machine to Machine) coraz mocniej wpływają na strukturę i funkcjonalność systemów sterowania stosowanych w maszynach, kształtując przy tym ideę Przemysłu 4.0 (Industry 4.0). Systemy sterowania zgodne z IoT wykorzystują sieci komunikacyjne, często o dużym stopniu komplikacji, łącząc poszczególne podzespoły, moduły, elementy wykonawcze i sensory. W artykule przedstawiono zagadnienie symulacji samoorganizacji ścieżek komunikacyjnych (trasowanie, routing) w złożonej sieci sensorycznej monitorującej działanie krążników przenośnika taśmowego. Poszczególne sensory tworzące sieć są niezależne i wyposażone w elektroniczny układ pomiarowy oraz transmisyjny MTU (Measuring and Transmitting Unit). W celu utworzenia i optymalizacji ścieżek transmisyjnych, w proponowanej strukturze komunikacyjnej, zaproponowano algorytm klasy SA (Swarm Algorithm) bazujący na zachowaniu roju.
Assumptions of an IoT (Internet of Things) and direct communication M2M (Machine to Machine) got strong influence on the structure and functionality of the control systems of machines, shaping at once an idea of the Industry 4.0 (Industry 4.0). All control systems, in accordance with the IoT, use communication networks, often with a high degree of complexity, combining the various components, modules, actuators and sensors. The paper presents the simulation problem of self-organization communication paths in a complex network of sensory monitoring of operation of the conveyor belt rollers, in which each sensor is equipped with an independent, electronic measuring and transmission unit (MTU). In order to create and optimize the communication structure an algorithm of class SA (Swarm Algorithm), based on the behavior of the swarm, was proposed.
Źródło:: Maszyny Górnicze; 2015, 33, 2; 3-8
0209-3693
2450-9442
Pojawia się w:: Maszyny Górnicze
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Allocation of real power generation based on computing over all generation cost: an approach of Salp Swarm Algorithm
Autorzy:: Devarapalli, Ramesh
Sinha, Nikhil Kumar
Rao, Bathina Venkateswara
Knypiński, Łukasz
Lakshmi, Naraharisetti Jaya Naga
García Márquez, Fausto Pedro
Powiązania:: https://bibliotekanauki.pl/articles/1841291.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: economic load dispatch
heuristic algorithms
optimization
Particle Swarm
Algorithm
Salp Swarm Algorithm
ekonomiczna wysyłka ładunku
algorytmy heurystyczne
optymalizacja
rój cząstek
algorytm
Opis:: Economic Load Dispatch (ELD) is utilized in finding the optimal combination of the real power generation that minimizes total generation cost, yet satisfying all equality and inequality constraints. It plays a significant role in planning and operating power systems with several generating stations. For simplicity, the cost function of each generating unit has been approximated by a single quadratic function. ELD is a subproblem of unit commitment and a nonlinear optimization problem. Many soft computing optimization methods have been developed in the recent past to solve ELD problems. In this paper, the most recently developed population-based optimization called the Salp Swarm Algorithm (SSA) has been utilized to solve the ELD problem. The results for the ELD problem have been verified by applying it to a standard 6-generator system with and without due consideration of transmission losses. The finally obtained results using the SSA are compared to that with the Particle Swarm Optimization (PSO) algorithm. It has been observed that the obtained results using the SSA are quite encouraging.
Źródło:: Archives of Electrical Engineering; 2021, 70, 2; 337-349
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: A novel variant of the salp swarm algorithm for engineering optimization
Autorzy:: Jia, Fuyun
Luo, Sheng
Yin, Guan
Ye, Yin
Powiązania:: https://bibliotekanauki.pl/articles/23944824.pdf
Data publikacji:: 2023
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: salp swarm algorithm
meta-heuristic algorithm
chaos theory
sine-cosine mechanism
quantum computation
optimization design of engineering
Opis:: There are many design problems need to be optimized in various fields of engineering, and most of them belong to the NP-hard problem. The meta-heuristic algorithm is one kind of optimization method and provides an effective way to solve the NP-hard problem. Salp swarm algorithm (SSA) is a nature-inspired algorithm that mimics and mathematically models the behavior of slap swarm in nature. However, similar to most of the meta-heuristic algorithms, the traditional SSA has some shortcomings, such as entrapment in local optima. In this paper, the three main strategies are adopted to strengthen the basic SSA, including chaos theory, sine-cosine mechanism and the principle of quantum computation. Therefore, the SSA variant is proposed in this research, namely SCQ-SSA. The representative benchmark functions are employed to test the performances of the algorithms. The SCQ-SSA are compared with the seven algorithms in high-dimensional functions (1000 dimensions), seven SSA variants and six advanced variants on benchmark functions, the experiment reveals that the SCQ-SSA enhances resulting precision and alleviates local optimal problems. Besides, the SCQ-SSA is applied to resolve three classical engineering problems: tubular column design problem, tension/compression spring design problem and pressure vessel design problem. The design results indicate that these engineering problems are optimized with high accuracy and superiority by the improved SSA. The source code is available in the URL: https://github.com/ye-zero/SCQSSA/tree/main/SCQ-SSA.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 131--149
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: Optimization of Square-shaped Bolted Joints Based on Improved Particle Swarm Optimization Algorithm
Autorzy:: Chen, Kui
Yang, Cheng
Zhao, Yongsheng
Niu, Peng
Niu, NaNa
Hongchao, Wu
Powiązania:: https://bibliotekanauki.pl/articles/27312779.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:: particle swarm optimization algorithm
bolt connection
bolted joint
fractal theory
Opis:: The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.
Źródło:: Eksploatacja i Niezawodność; 2023, 25, 3; art. no. 168487
1507-2711
Pojawia się w:: Eksploatacja i Niezawodność
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 12.

Tytuł:: Active power loss reduction by novel feral cat swarm optimization algorithm
Autorzy:: Lenin, Kanagasabai
Powiązania:: https://bibliotekanauki.pl/articles/384742.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: optimal reactive power
Transmission loss
Feral Cat Swarm Optimization Algorithm
Opis:: In this paper Feral Cat Swarm Optimization (FCS) Algorithm is proposed to solve optimal reactive power problem. Projected methodology has been modeled based on the activities of the feral cats. They have two main phases primarily “seeking mode”, “tracing mode”. In the proposed FCS algorithm, population of feral cats are created and arbitrarily scattered in the solution space, with every feral cat representing a solution. Produced population is alienated into two subgroups. One group will observe their surroundings which come under the seeking mode and another group moving towards the prey which will come under the tracing mode. New-fangled positions, fitness functions will be calculated subsequent to categorization of feral cats for seeking mode and tracing mode, through that cat with the most excellent solution will be accumulated in the memory. Feral Cat Swarm Optimization (FCS) Algorithm has been tested in standard IEEE 30 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 2; 25-29
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 13.

Tytuł:: Harmonogramowanie powtarzalnych procesów budowlanych z zastosowaniem algorytmu rojowego
Scheduling repetitive construction processes using a swarm algorithm
Autorzy:: Tomczak, Michał
Jaśkowski, Piotr
Powiązania:: https://bibliotekanauki.pl/articles/1857862.pdf
Data publikacji:: 2021
Wydawca:: Polski Związek Inżynierów i Techników Budownictwa
Tematy:: proces budowlany
harmonogramowanie
przedsięwzięcie budowlane
proces powtarzalny
algorytm rojowy
construction process
scheduling
construction project
repetitive process
swarm algorithm
Opis:: W artykule zaproponowano metodę optymalizacji trójkryterialnej harmonogramów powtarzalnych procesów budowlanych. Ze względu na trudności w projektowaniu realizacji tego typu przedsięwzięć z wykorzystaniem klasycznych narzędzi i metod zaproponowano wykorzystanie algorytmów rojowych do znajdowania niezdominowanych rozwiązań problemu. Zaprezentowano także przykład zastosowania algorytmu optymalizacji rojem cząstek do opracowania harmonogramu realizacji powtarzalnych procesów budowlanych i doboru brygad roboczych w celu minimalizacji czasu realizacji przedsięwzięcia i poszczególnych obiektów lub działek roboczych oraz przestojów w pracy brygad.
This paper proposes a method for tri-criteria optimization of schedules of repetitive construction processes. Due to the difficulties in designing the implementation of such projects using classical tools and methods, the use of swarm algorithms for finding non-dominated solutions to the problem was proposed. An example of the application of the particle swarm optimization algorithm to the development of a schedule for the realization of repetitive construction processes and the selection of work crews in order to minimize the execution time of the project and individual objects or work units as well as downtime in the work crews is also presented.
Źródło:: Przegląd Budowlany; 2021, 92, 7-8; 45-49
0033-2038
Pojawia się w:: Przegląd Budowlany
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 14.

Tytuł:: Stochastic Movement Swarm Performing a Coverage Task with Physical Parameters
Stochastyczny ruch roju wykonujący zadanie przeszukiwania z uwzględnieniem parametrów fizycznych
Autorzy:: Falkowski, Krzysztof
Duda, Michał
Powiązania:: https://bibliotekanauki.pl/articles/2135005.pdf
Data publikacji:: 2022
Wydawca:: Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:: swarms
swarm algorithm
sweep coverage
coverage task
optimisation
stochastic movement
algorytm roju
zasięg przemiatania
zadanie pokrycia
optymalizacja
ruch stochastyczny
Opis:: This paper describes an attempt of implementing physical parameters into a virtual swarm algorithm solution. It defines which physical parameters of the single object need to be known to properly transfer a virtual algorithm into a physical system. Considerations have been based on a stochastic movement swarm performing a coverage task. Time to finish the task and energy consumptions were measured for different numbers of drones in a swarm allowing to designate an optimal size of the swarm. Additional tests for changing variables allowed us to determine their impact on the swarm performance. The presented algorithm is a discrete-time solution, and every test is divided into steps. Positions of the drones are calculated only in time corresponding to these steps. Their position is unknown between these steps and the algorithm does not check if the paths of two drones cross between subsequent positions. The lower the time interval, the more precise results, but simulating the test requires more computing power. Further work should consider the smallest possible time intervals or additional feature to check if the paths of the drones do not cross.
W artykule opisano próbę implementacji parametrów fizycznych do rozwiązania algorytmu wirtualnego roju. Określono, które parametry fizyczne pojedynczego obiektu muszą być znane, aby poprawnie przenieść wirtualny algorytm do systemu fizycznego. Rozważania oparto na stochastycznym roju ruchu wykonującym zadanie przeszukiwania. Zmierzono czas wykonania zadania i zużycie energii dla różnej liczby dronów w roju, co pozwoliło na wyznaczenie optymalnej wielkości roju. Dodatkowe testy zmieniających się zmiennych pozwoliły określić ich wpływ na wydajność roju. Przedstawiony algorytm jest rozwiązaniem dyskretnym i z każdym testem jest podzielony na kroki. Pozycje dronów są obliczane tylko w czasie odpowiadającym tym krokom. Ich pozycja między tymi krokami jest nieznana, a algorytm nie sprawdza, czy ścieżki dwóch dronów przecinają się między kolejnymi pozycjami. Im krótszy odstęp czasu, tym dokładniejsze wyniki, ale symulacja testu wymaga większej mocy obliczeniowej. Dalsze prace powinny uwzględniać możliwie najmniejsze odstępy czasu lub dodatkową funkcję do sprawdzenia jeśli ścieżki dronów się nie przecinają.
Źródło:: Problemy Mechatroniki : uzbrojenie, lotnictwo, inżynieria bezpieczeństwa; 2022, 13, 3 (49); 9--26
2081-5891
Pojawia się w:: Problemy Mechatroniki : uzbrojenie, lotnictwo, inżynieria bezpieczeństwa
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 15.

Tytuł:: Intelligent self-powered sensors in the state-of-the-art control systems of mining machines
Inteligentne sensory samozasilające w nowoczesnych systemach sterowania maszyn górniczych
Autorzy:: Jasiulek, D.
Stankiewicz, K.
Woszczyński, M.
Powiązania:: https://bibliotekanauki.pl/articles/220125.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: odzysk energii z użyciem piezoelektryków
górnictwo
czujnik
termogenerator
algorytm roju
piezoelectric energy harvesters
coal mining
sensor
energy harvesting
thermoelectric generator
swarm algorithm
Opis:: Perspectives of development of control system dedicated for areas threatened by methane and/or coal dust explosion hazard are presented. Development of self-powered sensors, dedicated for operation in wireless network is one of the development directions. Such a solution will complement typical control systems and it can be used in the places, where there is no possibility of using the typical sensors, in close vicinity to the machine – due to lack of wired connection. General concept of the self-powered sensors with use of two methods of power supply – piezoelectric energy harvester and thermoelectric generator, is given. Perspective of using the methods of artificial intelligence in automatic configuration of sensors network is suggested.
W artykule przedstawiono perspektywy rozwoju systemów sterowania dedykowanych do przestrzeni zagrożonych wybuchem metanu i/lub pyłu węglowego. Jednym z kierunków rozwoju tych systemów jest opracowanie systemu czujników samozasilających, dedykowanych do pracy w sieciach bezprzewodowych. Rozwiązanie takie będzie stanowić uzupełnienie typowych układów sterowania, możliwe do zastosowania w miejscach, w których nie istnieje możliwość zainstalowania czujników konwencjonalnych lub w bezpośrednim otoczeniu maszyny, w przypadku braku możliwości połączenia przewodowego. W artykule została przedstawiona ogólna koncepcja sieci czujników samozasilających z uwzględnieniem dwóch metod zasilania – z zastosowaniem piezoelectric energy harvester (odzysk energii z użyciem piezoelektryków) oraz termogeneratorów. Przedstawiona została również perspektywa zastosowania metod sztucznej inteligencji w automatycznej konfiguracji złożonej sieci komunikacyjnej obejmującej przedmiotowe oczujnikowanie.
Źródło:: Archives of Mining Sciences; 2016, 61, 4; 907-915
0860-7001
Pojawia się w:: Archives of Mining Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 16.

Tytuł:: Design of a Predictive PID Controller Using Particle Swarm Optimization
Autorzy:: Mustafa, Norhaida
Hashim, Fazida Hanim
Powiązania:: https://bibliotekanauki.pl/articles/1844451.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: proportional integral derivative controller
particle swarm optimization (PSO) algorithm
optimization
predictive PID
Opis:: The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds).
Źródło:: International Journal of Electronics and Telecommunications; 2020, 66, 4; 737-743
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 17.

Tytuł:: A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO
Autorzy:: Nagaraj, B.
Vijayakumar, P.
Powiązania:: https://bibliotekanauki.pl/articles/384767.pdf
Data publikacji:: 2011
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: ant colony algorithm
evolutionary program
genetic algorithm particle swarm optimization and soft computing
Opis:: Proportional - Integral - Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. How ever PID controller are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This article comes up with a hybrid approach involving Genetic Algorithm (GA), Evolutionary Pro gramming (EP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The proposed hybrid algorithm is used to tune the PID parameters and its per formance has been compared with the conventional me thods like Ziegler Nichols and Cohen Coon method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. Speed control of DC motor process is used to assess the efficacy of the heuristic algorithm methodology
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 2; 42-48
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 18.

Tytuł:: Modelling of Curvature of the Railway Track Geometrical Layout Using Particle Swarm Optimization
Autorzy:: Palikowska, Katarzyna Małgorzata
Powiązania:: https://bibliotekanauki.pl/articles/504485.pdf
Data publikacji:: 2014
Wydawca:: Międzynarodowa Wyższa Szkoła Logistyki i Transportu
Tematy:: Particle Swarm Optimization algorithm cubic C-Bezier curve
curvature of the railway track layout dynamic interactions
transition curve
Opis:: A method of railway track geometrical layout design, based on an application of cubic C-Bezier curves for describing the layout curvature is presented in the article. The control points of a cubic C-Bezier curve are obtained in an optimization process carried out using Particle Swarm Optimization algorithm. The optimization criteria are based on the evaluation of the dynamic interactions and satisfaction of geometrical design requirements.
Źródło:: Logistics and Transport; 2014, 21, 1; 73-82
1734-2015
Pojawia się w:: Logistics and Transport
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 19.

Tytuł:: Studium przypadku skuteczności nowych metod optymalizacji roju w porównaniu do metod znanych
Autorzy:: Baumgart, Jan
Sangho, Belco
Powiązania:: https://bibliotekanauki.pl/articles/41206153.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Kazimierza Wielkiego w Bydgoszczy
Tematy:: algorytm roju
wzorce
inspirowanie naturą
metaheurystyka
pojedyncze obiektywne problemy optymalizacji
optymalizacja funkcji
algorytmy optymalizacji
swarm algorithm
patterns
inspired by nature
metaheuristics
single objective optimization problems
function optimization
optimization algorithms
Opis:: Porównianie skuteczności nowych metod optymalizacji roju w porównaniu z metodami znanymi w dziedzinie. Inspirowane naturą algorytmy metaheurystyczne stają się coraz bardziej popularne w rozwiązywaniu problemów optymalizacyjnych. Dzięki ich popularności niemal codziennie możemy zobaczyć nowepodejścia i proponowane rozwiązania. W tym artykule przedstawię porównanie, które pokaże kilka najnowszychprac z tej dziedziny w porównaniu z niektórymi algorytmami traktowanymi jako podstawa dziedziny. Głównymcelem było porównanie ostatnio wprowadzonych algorytmów roju i określenie, kiedy nowe rozwiązania są faktycznie szybsze i bardziej precyzyjne. Podsumowując, czy przetestowane nowe podejścia są lepsze niż obecne,dobrze znane i ugruntowane w terenie algorytmy. Algorytmy brane pod uwagę w tej pracy to: Particle SwarmOptimization [5], Artifical Bee Colony [3], Elephant Herding Optimization [7], Whale Optimization [4] i Gras-shopper Optimization [6].Algorytmy uznawane za nowe w tej dziedzinie porównano z dwoma popularnymi idobrze znanymi algorytmami metaheurystycznymi pod względem dokładności znalezionych rozwiązań i szybkości. Zgodnie z wynikami eksperymentów większość porównywanych nowych algorytmów dawała zadowalającewyniki w użytkowaniu.
Comparing the effectiveness of new methods of swarm optimization in comparison with knownmethods. Nature-inspired metaheuristic algorithms are becoming more and more popular in solving optimization problems. Thanks to their popularity, we can see new approaches and proposed solutions almost everyday. In this article, I will present a comparison that will show some of the most recent works in this fieldcompared to some algorithms considered as the basis of the field. The main goal was to compare the recently introduced swarm algorithms and determine when new solutions are actually faster and more precise. Inconclusion, are the new approaches tested better than the current, well-known and field-grounded algorithms?The algorithms considered in this paper are Particle Swarm Optimization, Artifical Bee Colony, Elephant Herding Optimization, Whale Optimization, and Grasshopper Optimization. Algorithms considered new inthis field were compared with two popular and well-known metaheuristic algorithms in terms of accuracy ofsolutions found and speed. According to the experimental results, most of the compared new algorithms gave satisfactory results in use.
Źródło:: Studia i Materiały Informatyki Stosowanej; 2021, 1; 47-50
1689-6300
Pojawia się w:: Studia i Materiały Informatyki Stosowanej
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 20.

Tytuł:: Predicting and minimizing the blasting cost in limestone mines using a combination of gene expression programming and particle swarm optimization
Autorzy:: Bastami, Reza
Bazzazi, Abbas Aghajani
Shoormasti, Hadi Hamidian
Ahangari, Kaveh
Powiązania:: https://bibliotekanauki.pl/articles/1853861.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: kopalnia wapienia
wybuch detonacyjny
regresja nieliniowa
blasting cost
limestone mine
gene expression programming
non-linear multivariate regression
particle swarm optimization algorithm
environmental impacts
Opis:: Blasting cost prediction and optimization is of great importance and significance to achieve optimal fragmentation through controlling the adverse consequences of the blasting process. By gathering explosive data from six limestone mines in Iran, the present study aimed to develop a model to predict blasting cost, by gene expression programming method. The model presented a higher correlation coefficient (0.933) and a lower root mean square error (1088) comparing to the linear and nonlinear multivariate regression models. Based on the sensitivity analysis, spacing and ANFO value had the most and least impact on blasting cost, respectively. In addition to achieving blasting cost equation, the constraints such as frag-mentation, fly rock, and back break were considered and analyzed by the gene expression programming method for blasting cost optimization. The results showed that the ANFO value was 9634 kg, hole dia-meter 76 mm, hole number 398, hole length 8.8 m, burden 2.8 m, spacing 3.4 m, hardness 3 Mhos, and uniaxial compressive strength 530 kg/cm2 as the blast design parameters, and blasting cost was obtainedas 6072 Rials/ton, by taking into account all the constraints. Compared to the lowest blasting cost among the 146-research data (7157 Rials/ton), this cost led to a 15.2% reduction in the blasting cost and optimal control of the adverse consequences of the blasting process.
Źródło:: Archives of Mining Sciences; 2020, 65, 4; 835-850
0860-7001
Pojawia się w:: Archives of Mining Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 21.

Tytuł:: A comparative study on multi-swarm optimisation and bat algorithm for unconstrained non linear optimisation problems
Autorzy:: Baidoo, E.
Opoku Oppong, S
Powiązania:: https://bibliotekanauki.pl/articles/117918.pdf
Data publikacji:: 2016
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: swarm intelligence
bio-inspired
bat algorithm
multi-swarm optimisation
nonlinear optimisation
Opis:: A study branch that mocks-up a population of network of swarms or agents with the ability to self-organise is Swarm intelligence. In spite of the huge amount of work that has been done in this area in both theoretically and empirically and the greater success that has been attained in several aspects, it is still ongoing and at its infant stage. An immune system, a cloud of bats, or a flock of birds are distinctive examples of a swarm system. In this study, two types of meta-heuristics algorithms based on population and swarm intelligence - Multi Swarm Optimization (MSO) and Bat algorithms (BA) – are set up to find optimal solutions of continuous non-linear optimisation models. In order to analyze and compare perfect solutions at the expense of performance of both algorithms, a chain of computational experiments on six generally used test functions for assessing the accuracy and the performance of algorithms, in swarm intelligence fields are used. Computational experiments show that MSO algorithm seems much superior to BA.
Źródło:: Applied Computer Science; 2016, 12, 4; 59-77
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 22.

Tytuł:: Population diversity maintenance in brain storm optimization algorithm
Autorzy:: Cheng, S.
Shi, Y.
Qin, Q.
Zhang, Q
Bai, R.
Powiązania:: https://bibliotekanauki.pl/articles/91571.pdf
Data publikacji:: 2014
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: brainstorm
optimization algorithm
convergence
divergence
brainstorm optimization
BSO
swarm intelligence
BSO algorithm
Opis:: The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm’s exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 83-97
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 23.

Tytuł:: Voice recognition through the use of Gabor transform and heuristic algorithm
Autorzy:: Woźniak, M.
Połap, D.
Powiązania:: https://bibliotekanauki.pl/articles/226687.pdf
Data publikacji:: 2017
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: neural networks
voice recognition
Gabor transform
heuristic algorithm
swarm
Opis:: Increasingly popular use of verification methods based on specific characteristics of people like eyeball, fingerprint or voice makes inventing more accurate and irrefutable methods of that urgent. In this work we present voice verification based on Gabor transformation. Proposed approach involves creation of spectrogram, which serves as a habitat for the population in selected heuristic algorithm. The use of heuristic allows for feature extraction to enable identity verification using classical neural network. The results of the research are presented and discussed to show efficiency of the proposed methodology.
Źródło:: International Journal of Electronics and Telecommunications; 2017, 63, 2; 159-164
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 24.

Tytuł:: On the efficiency of population-based optimization in finding best parameters for RGB-D visual odometry
Autorzy:: Kostusiak, Aleksander
Skrzypczyński, Piotr
Powiązania:: https://bibliotekanauki.pl/articles/384397.pdf
Data publikacji:: 2019
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: particle swarm optimization (PSO)
evolutionary algorithm
visual odometry
RGB-D
Opis:: Visual odometry estimates the transformations between consecutive frames of a video stream in order to recover the camera’s trajectory. As this approach does not require to build a map of the observed environment, it is fast and simple to implement. In the last decade RGBD cameras proliferated in roboTIcs, being also the sensors of choice for many practical visual odometry systems. Although RGB-D cameras provide readily available depth images, that greatly simplify the frame-to-frame transformations computaTIon, the number of numerical parameters that have to be set properly in a visual odometry system to obtain an accurate trajectory estimate remains high. Whereas seƫng them by hand is certainly possible, it is a tedious try-and-error task. Therefore, in this article we make an assessment of two population-based approaches to parameter opTImizaTIon, that are for long time applied in various areas of robotics, as means to find best parameters of a simple RGB-D visual odometry system. The optimization algorithms investigated here are particle swarm optimization and an evolutionary algorithm variant. We focus on the optimization methods themselves, rather than on the visual odometry algorithm, seeking an efficient procedure to find parameters that minimize the estimated trajectory errors. From the experimental results we draw conclusions as to both the efficiency of the optimization methods, and the role of particular parameters in the visual odometry system.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2019, 13, 2; 5-14
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 25.

Tytuł:: Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms
Autorzy:: Olejarczyk-Wożeńska, Izabela
Opaliński, Andrzej
Mrzygłód, Barbara
Regulski, Krzysztof
Kurowski, Wojciech
Powiązania:: https://bibliotekanauki.pl/articles/29520068.pdf
Data publikacji:: 2022
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: heuristic optimization
bainite
ADI
Particle Swarm Optimization
Evolutionary Optimization Algorithm
Opis:: The paper presents the application of heuristic optimization methods in identifying the parameters of a model for bainite transformation time in ADI (Austempered Ductile Iron). Two algorithms were selected for parameter optimization – Particle Swarm Optimization and Evolutionary Optimization Algorithm. The assumption of the optimization process was to obtain the smallest normalized mean square error (objective function) between the time calculated on the basis of the identified parameters and the time derived from the experiment. As part of the research, an analysis was also made in terms of the effectiveness of selected methods, and the best optimization strategies for the problem to be solved were selected on their basis.
Źródło:: Computer Methods in Materials Science; 2022, 22, 3; 125-136
2720-4081
2720-3948
Pojawia się w:: Computer Methods in Materials Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 26.

Tytuł:: Fireworks Algorithm for Unconstrained Function Optimization Problems
Autorzy:: Baidoo, E.
Powiązania:: https://bibliotekanauki.pl/articles/117784.pdf
Data publikacji:: 2017
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: Fireworks algorithm
Function optimization
swarm intelligence
Mathematical programming
Natural computing
Opis:: Modern real world science and engineering problems can be classified as multi-objective optimisation problems which demand for expedient and efficient stochastic algorithms to respond to the optimization needs. This paper presents an object-oriented software application that implements a firework optimization algorithm for function optimization problems. The algorithm, a kind of parallel diffuse optimization algorithm is based on the explosive phenomenon of fireworks. The algorithm presented promising results when compared to other population or iterative based meta-heuristic algorithm after it was experimented on five standard ben-chmark problems. The software application was implemented in Java with interactive interface which allow for easy modification and extended expe-rimentation. Additionally, this paper validates the effect of runtime on the al-gorithm performance.
Źródło:: Applied Computer Science; 2017, 13, 1; 61-74
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 27.

Tytuł:: On the hybridization of the artificial Bee Colony and Particle Swarm Optimization Algorithms
Autorzy:: El-Abd, M.
Powiązania:: https://bibliotekanauki.pl/articles/91658.pdf
Data publikacji:: 2012
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: Artificial Bee Colony Algorithm
ABC
particle swarm optimization (PSO)
PSO
hybridization
hybrid algorithm
CEC05
Opis:: In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one, where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Three different versions of the hybrid algorithm are tested in this work by experimenting with different selection mechanisms for the ABC component. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics, namely, the solution reached, the success rate, and the performance rate.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 2; 147-155
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 28.

Tytuł:: Function optimization using metaheuristics
Autorzy:: Pilski, M.
Seredyński, F.
Powiązania:: https://bibliotekanauki.pl/articles/92887.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:: particle swarm optimization (PSO)
artificial immune system
genetic algorithm
function optimization
Opis:: The paper presents the results of comparison of three metaheuristics that currently exist in the problem of function optimization. The first algorithm is Particle Swarm Optimization (PSO) - the algorithm has recently emerged. The next one is based on a paradigm of Artificial Immune System (AIS). Both algorithms are compared with Genetic Algorithm (GA). The algorithms are applied to optimize a set of functions well known in the area of evolutionary computation. Experimental results show that it is difficult to unambiguously select one best algorithm which outperforms other tested metaheuristics.
Źródło:: Studia Informatica : systems and information technology; 2006, 1(7); 77-91
1731-2264
Pojawia się w:: Studia Informatica : systems and information technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 29.

Tytuł:: A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:: Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Powiązania:: https://bibliotekanauki.pl/articles/1837533.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function
Opis:: The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 30.

Tytuł:: Performance Comparison of Optimization Methods for Flat-Top Sector Beamforming in a Cellular Network
Autorzy:: Nandi, Pampa
Roy, Jibendu Sekhar
Powiązania:: https://bibliotekanauki.pl/articles/2142316.pdf
Data publikacji:: 2022
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: flat-top sector beam
particle swarm optimization
real-coded genetic algorithm
Opis:: The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.
Źródło:: Journal of Telecommunications and Information Technology; 2022, 3; 39--46
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 31.

Tytuł:: Intelligent algorithms for routing sensory networks operating in explosion hazard zones
Autorzy:: Stankiewicz, Krzysztof
Jagoda, Jerzy
Tonkins, Matthew
Powiązania:: https://bibliotekanauki.pl/articles/2016485.pdf
Data publikacji:: 2021
Wydawca:: Politechnika Wrocławska. Wydział Geoinżynierii, Górnictwa i Geologii. Instytut Górnictwa
Tematy:: routing algorithm
Internet of Things
explosion hazardous area
sensor network
swarm intelligence
Opis:: The article presents intelligent routing algorithms currently used in sensory networks, in terms of determining the possibility of their integration into systems working in potentially explosive atmospheres. Selected types of scribing algorithms were characterized. The analysis of simulation tests performed on selected types of scribing algorithms was carried out. The analysis of equipment solutions which can be used to build a network node operating in the conditions of methane and/or coal dust explosion hazard was carried out.
Źródło:: Mining Science; 2021, 28; 103-115
2300-9586
2353-5423
Pojawia się w:: Mining Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 32.

Tytuł:: FLC control for tuning exploration phase in bio-inspired metaheuristic
Autorzy:: Kiełkowicz, K.
Grela, D.
Powiązania:: https://bibliotekanauki.pl/articles/106299.pdf
Data publikacji:: 2016
Wydawca:: Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:: Bat algorithm
swarm intelligence
metaheuristics
optimization
fuzzy logic
Mamdami-Type inference system
Opis:: Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.
Źródło:: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2016, 16, 2; 32-38
1732-1360
2083-3628
Pojawia się w:: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 33.

Tytuł:: A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm
Autorzy:: Shial, Gyanaranjan
Saho, Sabita
Panigrahi, Sibarama
Powiązania:: https://bibliotekanauki.pl/articles/27312857.pdf
Data publikacji:: 2023
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: grey wolf optimizer
JAYA algorithm
article swarm optimization
ine-cosinealgorithm
partitional clustering
Opis:: This paper presents a hybrid meta-heuristic algorithm that uses the grey wolfoptimization (GWO) and the JAYA algorithm for data clustering. The ideais to use the explorative capability of the JAYA algorithm in the exploitativephase of GWO to form compact clusters. Here, instead of using only one bestand one worst solution for generating offspring, the three best wolves (alpha,beta and delta) and three worst wolves of the population are used. So, the bestand worst wolves assist in moving towards the most feasible solutions and simul-taneously it helps to avoid from worst solutions; this enhances the chances oftrapping at local optimal solutions. The superiority of the proposed algorithmis compared with five promising algorithms; namely, the sine-cosine (SCA),GWO, JAYA, particle swarm optimization (PSO), and k-means algorithms.The performance of the proposed algorithm is evaluated for 23 benchmarkmathematical problems using the Friedman and Nemenyi hypothesis tests. Ad-ditionally, the superiority and robustness of our proposed algorithm is testedfor 15 data clustering problems by using both Duncan's multiple range test andthe Nemenyi hypothesis test.
Źródło:: Computer Science; 2023, 24 (3); 361--405
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 34.

Tytuł:: Algorytmy stadne w problemach optymalizacji
Swarm Algorithms in Optimization Problems
Autorzy:: Filipowicz, B.
Kwiecień, J.
Powiązania:: https://bibliotekanauki.pl/articles/274567.pdf
Data publikacji:: 2011
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: optymalizacja nieliniowa
algorytm PSO
algorytm pszczeli
algorytm świetlika
nonlinear optimization
particle swarm optimization (PSO)
bee algorithm
firefly algorithm
Opis:: W artykule przedstawiono zastosowanie algorytmu optymalizacji rojem cząstek, algorytmu pszczelego i algorytmu świetlika do wyznaczenia optymalnego rozwiązania wybranych testowych funkcji ciągłych. Przedstawiono i porównano wyniki badań dla funkcji Rosenbrocka, Rastrigina i de Jonga.
This paper presents particle swarm optimization, bee algorithm and firefly algorithm, used for optimal solution of selected continuous well-known functions. Results of these algorithms are compared to each other on Rosenbrock, Rastrigin and de Jong functions.
Źródło:: Pomiary Automatyka Robotyka; 2011, 15, 12; 152-157
1427-9126
Pojawia się w:: Pomiary Automatyka Robotyka
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 35.

Tytuł:: Economic dispatch in power system networks including renewable energy resources using various optimization techniques
Autorzy:: Hafiz, Abrar Mohamed
Abdelrahman, M. Ezzat
Temraz, Hesham
Powiązania:: https://bibliotekanauki.pl/articles/1841222.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: Economic Dispatch (ED)
Particle Swarm Optimization (PSO)
Sine-Cosine
Algorithm (SCA)
Photovoltaic (PV)
Opis:: Economic dispatch (ED) is an essential part of any power system network. ED is how to schedule the real power outputs from the available generators to get the minimum cost while satisfying all constraints of the network. Moreover, it may be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED, e.g., Newton-Raphson method Lambda-Iterative technique, Gaussian-Seidel method, etc. All these traditional methods need the generators’ incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and solar are a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, it is attempted to enhance the operation of electrical power system networks via economic dispatch. An ED problem is solved using various techniques, e.g., Particle Swarm Optimization (PSO) technique and Sine-Cosine Algorithm (SCA). Afterwards, the results are compared. Moreover, case studies are executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses are performed on MATLAB software.
Źródło:: Archives of Electrical Engineering; 2021, 70, 3; 643-655
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 36.

Tytuł:: Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation
Autorzy:: Pandiarajan, K.
Babulal, C. K.
Powiązania:: https://bibliotekanauki.pl/articles/141059.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: non-dominated sorting genetic algorithm
generation rescheduling
particle swarm optimization (PSO)
differential evolution
overload index
Opis:: This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem
Źródło:: Archives of Electrical Engineering; 2014, 63, 3; 367-384
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 37.

Tytuł:: Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem
Autorzy:: Białogłowski, M.
Staniaszek, M.
Laskowski, W.
Grudniak, M.
Powiązania:: https://bibliotekanauki.pl/articles/91276.pdf
Data publikacji:: 2018
Wydawca:: Warszawska Wyższa Szkoła Informatyki
Tematy:: traveling salesman problem
Nearest Neighbor
Monte Carlo
Simulated Annealing
Genetic Algorithm
particle swarm optimization (PSO)
Opis:: We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.
Źródło:: Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
1896-396X
2082-8349
Pojawia się w:: Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 38.

Tytuł:: Porównanie wybranych algorytmów wilczego stada stosowanych w rozwiązaniach problemów optymalizacji
Comparison of selected wolf pack algorithms used in solving optimization problems
Autorzy:: Sangho, Belco
Powiązania:: https://bibliotekanauki.pl/articles/41206104.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Kazimierza Wielkiego w Bydgoszczy
Tematy:: optymalizacja
algorytmy rojowe
algorytmy wilcze
wilki
funkcje porównujące
optimization
swarm algorithms
wolf herd algorithm
wolfs
benchmarks
Opis:: Algorytmy optymalizacyjne zyskały uznanie jako szybki i konsekwentny sposób rozwiązywania problemów optymalizacyjnych. W ostatnim czasie wilki są coraz częściej wykorzystywane jako inspiracja do tworzenia algorytmów, jak i w projektach używających tych algorytmów. W niniejszej pracy opisano sześć wybranych algorytmów. Następnie zaimplementowano je w języku R i porównano z pomocą sześciu funkcji porównujących, tzw. benchmarków. Wyniki trzydziestu testów na każdej z funkcji zaprezentowano za pomocą średniego wyniku, odchylenia standardowego wyniku, średniego czasu oraz odchylenia standardowego czasu. Dodatkowo zaprezentowano wykres zbieżności na dwóch z funkcji porównujących. Uzyskane wyniki algorytmów często różniły się od tych zaprezentowanych w publikacjach, jednak skuteczność części z nich była lepsza bądź porównywalna z PSO[1], DE[2] i GA[3]. Najlepszym wilczym algorytmem okazał się Grey Wolf Optimizer[4].
Optimization algorithms have gained recognition as a fast and consistent way to solve optimization problems. Recently, wolves have been increasingly used as inspiration for algorithms as well as in projects using these algorithms. In this paper, six selected algorithms are described. They were then implemented in R and compared using six comparison functions, called benchmarks. The results of thirty tests on each function were presented by mean score, standard deviation of the score, mean time and standard deviation of the time. Additionally, a convergence plot on two of the benchmark functions was presented. The algorithm results obtained often differed from those presented in the publications, but the performance of some of the algorithms was better or comparable to PSO[1], DE[2], and GA[3]. The best wolf algorithm was found to be Grey Wolf Optimizer[4].
Źródło:: Studia i Materiały Informatyki Stosowanej; 2021, 1; 17-32
1689-6300
Pojawia się w:: Studia i Materiały Informatyki Stosowanej
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 39.

Tytuł:: Comparative Study of Optimised Artificial Intelligence Based First Order Sliding Mode Controllers for Position Control of a DC Motor Actuator
Autorzy:: Nyong-Bassey, B. E.
Akinloye, B.
Powiązania:: https://bibliotekanauki.pl/articles/385114.pdf
Data publikacji:: 2016
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: adaptive fuzzy control
DC motor position control
genetic algorithm
particle swarm optimization (PSO)
sliding mode control
Opis:: This paper aims at critically reviewing various sliding mode control measures applied to Permanent Magnet DC Motor actuator for position control. At first, a hybrid sliding mode controller was examined with its advantages and disadvantages. Then, the smooth sliding mode controller in the same manner. The shortcomings of the two methods were overcome by proper switch design and also using tanh-sinh hyperbolic function. The sliding mode controller switches on when either disturbance or noise is detected. Genetic Algorithm Computational tuning technique is employed to optimize the gains of the controllers for optimal response.The performance of the proposed controller architecture, as well as the reviewed controllers, have been compared for performance evaluation with respect to several operating conditions. This includes load torque disturbance injection, noise injection in a feedback loop, motor nonlinearity exhibited by parameters variation, and a step change in reference input demand.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 3; 58-71
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 40.

Tytuł:: An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system
Autorzy:: Kumar, V. E.
Jerome, J.
Powiązania:: https://bibliotekanauki.pl/articles/141105.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: inverted pendulum
LQR controller
particle swarm optimization (PSO)
genetic algorithm
adaptive inertia weight factor
state feedback control
Opis:: This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.
Źródło:: Archives of Electrical Engineering; 2014, 63, 3; 345-365
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 41.

Tytuł:: Zastosowanie półautomatycznego algorytmu doboru optymalnej liczby i położenia odwiertów wydobywczych
Semi-Automatic Algorithm for Optimal Production Well Placement
Autorzy:: Łętkowski, P.
Powiązania:: https://bibliotekanauki.pl/articles/1835267.pdf
Data publikacji:: 2018
Wydawca:: Instytut Nafty i Gazu - Państwowy Instytut Badawczy
Tematy:: optymalizacja
algorytm nietoperza
położenie odwiertów
NPV
eksploatacja
algorytmy rojowe
optimization
bat algorithm
location of wells
exploitation
swarm algorithms
Opis:: Artykuł poświęcono zastosowaniu tzw. algorytmu nietoperza do rozwiązania problemu określenia optymalnej liczby i położenia odwiertów wydobywczych. W procesie optymalizacji jako funkcję celu wykorzystano bieżącą wartość netto (ang. net present value – NPV). Testy zbudowanego algorytmu przeprowadzono na przykładzie modelu symulacyjnego złoża PUNQ-S3, dostępne- go na zasadach open source. Zastosowany algorytm został wyposażony w dodatkowe mechanizmy zwiększające jego efektywność: mechanizm próbkowania sześcianu łacińskiego (ang. Latin hypercube sampling – LHS) oraz mechanizm eliminowania położeń odwiertów poza modelem. Przeprowadzone testy wskazują na bardzo dobrą zbieżność zbudowanego algorytmu w procesie optymalizacji.
The article is devoted to the application of the so-called bat algorithm to solve the problem of determining the optimum number and location of production wells. This algorithm was proposed by Yang in 2010, and since then has been successfully used in solving both theoretical and practical optimization problems. The method belongs to a group of swarm optimization methods and in searching for the best solution, the algorithm uses a mechanism of echolocation, similar to the one used by a herd of bats. The current net present value (NPV) was used as a target function in the optimization process. The algorithm was tested on the example of the simulation model of the PUNQ-S3 reservoir available on an OpenSource basis. The applied algorithm was equipped with additional mechanisms increasing its effectiveness: Latin Hypercube Sampling (LHS) algorithm and the mechanism eliminating the locations of wells outside the operational area of the model. The first of the applied improvements ensures a better starting point for the proper optimization process, which significantly improves the convergence of the whole algorithm. The latter mechanism solves a problem specific to the issue in question.
Źródło:: Nafta-Gaz; 2018, 74, 8; 598-605
0867-8871
Pojawia się w:: Nafta-Gaz
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 42.

Tytuł:: Optimizing the Bit-flipping Method for Decoding Low-density Parity-check Codes in Wireless Networks by Using the Artificial Spider Algorithm
Autorzy:: Ghaffoori, Ali Jasim
Abdul-Adheem, Wameedh Riyadh
Powiązania:: https://bibliotekanauki.pl/articles/2055251.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: low-density parity-check
LDPC
hard-decision Bit-Flipping
BF
particle swarm optimization
PSO
artificial spider algorithm
ASA
Opis:: In this paper, the performance of Low-Density Parity-Check (LDPC) codes is improved, which leads to reduce the complexity of hard-decision Bit-Flipping (BF) decoding by utilizing the Artificial Spider Algorithm (ASA). The ASA is used to solve the optimization problem of decoding thresholds. Two decoding thresholds are used to flip multiple bits in each round of iteration to reduce the probability of errors and accelerate decoding convergence speed while improving decoding performance. These errors occur every time the bits are flipped. Then, the BF algorithm with a low-complexity optimizer only requires real number operations before iteration and logical operations in each iteration. The ASA is better than the optimized decoding scheme that uses the Particle Swarm Optimization (PSO) algorithm. The proposed scheme can improve the performance of wireless network applications with good proficiency and results. Simulation results show that the ASA-based algorithm for solving highly nonlinear unconstrained problems exhibits fast decoding convergence speed and excellent decoding performance. Thus, it is suitable for applications in broadband wireless networks.
Źródło:: International Journal of Electronics and Telecommunications; 2022, 68, 1; 109--114
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 43.

Tytuł:: Badanie i analiza algorytmów rojowych w optymalizacji parametrów regulatora kursu statku
Study and analysis of swarm intelligence in optimizing parameters of the ship course controller
Autorzy:: Tomera, M.
Powiązania:: https://bibliotekanauki.pl/articles/266857.pdf
Data publikacji:: 2015
Wydawca:: Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
Tematy:: algorytmy rojowe
algorytm genetyczny
optymalizacja stochastyczna
regulator PID
sterowanie statkiem
swarm intelligence
genetic algorithm
random optimization
PID controller
ship control
Opis:: W pracy przedstawione zostały badania i analiza zastosowania wybranych algorytmów rojowych do optymalizacji parametrów regulatora PID w układzie sterowania statkiem na kursie. Optymalizacja ta polegała na minimalizacji czasowego wskaźnika jakości wyznaczanego na podstawie odpowiedzi skokowej. Do optymalizacji parametrów regulatora kursu statku wykorzystane zostały algorytmy rojowe, takie jak: algorytm mrówkowy, zmodyfikowany algorytm mrówkowy, algorytm sztucznej kolonii pszczół oraz algorytm optymalizacji rojem cząstek. Przeprowadzone zostały badania szybkości znajdowania optymalnego rozwiązania i wykonana została analiza porównawcza uzyskanych wyników. Zaprezentowane wyniki badań pozwalają stwierdzić, że algorytm optymalizacji rojem cząstek charakteryzuje się najlepszą jakością optymalizacji parametrów regulatora kursu statku.
The paper presents the research and analysis of the use of certain swarm intelligence algorithms to optimize the parameters of PID control in a ship on the course. This optimization was to minimize the performance quality index based on step response of the mathematical model of control system. To optimize the parameters of the ship course controller have been used swarm intelligence algorithms, such as: ant colony algorithm (ACO), the modified ant colony algorithm (MACO), the artificial bee colony algorithm (ABC) and the particle swarm optimization algorithm (PSO). Rate tests were conducted to find the optimal solution and a comparative analysis of the results was made. The presented results of research allow us to conclude that the particle swarm optimization (PSO) algorithm has the best quality of optimizing the control parameters of the course controller.
Źródło:: Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2015, 46; 103-106
1425-5766
2353-1290
Pojawia się w:: Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 44.

Tytuł:: Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction
Autorzy:: Pitchandi, Perumal
Powiązania:: https://bibliotekanauki.pl/articles/2201730.pdf
Data publikacji:: 2022
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: outliers removal
culturalartefact objects
3D reconstruction
particle swarm optimization
PSO
spatial clustering
density based spatial clustering
noise clustering algorithm
Opis:: In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information. These repositories include personal, historic, cultural, and business event images. Image mining is a limited field in research where most techniques look at processing images instead of mining. Very limited tools are found for mining these images, specifically 3D (Three Dimensional) images. Open source image datasets are not structured making it difficult for query based retrievals. Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D. This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors. Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects. Corresponding cultural 3D images are grouped for a 3D reconstruction engine’s optimized performance. Spatial clustering techniques based on density like PVDBSCAN (Particle Varied Density Based Spatial Clustering of Applications with Noise) eliminate image outliers. Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers. Clustering based on Information theory is also used in this work to identify cultural object’s image views which are then reconstructed using 3D motions. The proposed scheme is benchmarked with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to prove the proposed scheme’s efficiency. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.
Źródło:: Advances in Science and Technology. Research Journal; 2022, 16, 3; 269--281
2299-8624
Pojawia się w:: Advances in Science and Technology. Research Journal
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 45.

Tytuł:: A nature inspired collision avoidance algorithm for ships
Autorzy:: Lazarowska, A.
Powiązania:: https://bibliotekanauki.pl/articles/24201448.pdf
Data publikacji:: 2023
Wydawca:: Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:: collision avoidance algorithm
safe own Ship's Trajectory
safe navigation
ant colony optimization
firefly agorithm
path planning
swarm intelligence
nature inspired computing
Opis:: Nature inspired algorithms are regarded as a powerful tool for solving real life problems. They do not guarantee to find the globally optimal solution, but can find a suboptimal, robust solution with an acceptable computational cost. The paper introduces an approach to the development of collision avoidance algorithms for ships based on the firefly algorithm, classified to the swarm intelligence methods. Such algorithms are inspired by the swarming behaviour of animals, such as e.g. birds, fish, ants, bees, fireflies. The description of the developed algorithm is followed by the presentation of simulation results, which show, that it might be regarded as an efficient method of solving the collision avoidance problem. Such algorithm is intended for use in the Decision Support System or in the Collision Avoidance Module of the Autonomous Navigation System for Maritime Autonomous Surface Ships.
Źródło:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 2; 341--346
2083-6473
2083-6481
Pojawia się w:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 46.

Tytuł:: Cross‐Comparison of Evolutionary Algorithms for Optimizing Design of Sustainable Supply Chain Network under Disruption Risks
Autorzy:: Al-Zuheri, Atiya
Powiązania:: https://bibliotekanauki.pl/articles/2023790.pdf
Data publikacji:: 2021
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: comparison
genetic algorithm
particle swarm optimization
sustainable supply chain design
disruption risk
porównanie
algorytm genetyczny
optymalizacja rojem cząstek
projektowanie zrównoważonego łańcucha dostaw
ryzyko zakłóceń
Opis:: Optimization of a sustainable supply chain network design (SSCND) is a complex decision-making process which can be done by the optimal determination of a set of decisions and constraints such as the selection of suppliers, transportation-related facilities and distribution centres. Different optimization techniques have been applied to handle various SSCND problems. Meta- heuristic algorithms are developed from these techniques that are commonly used to solving supply chain related problems. Among them, Genetic algorithms (GA) and particle swarm optimization (PSO) are implemented as optimization solvers to obtain supply network design decisions. This paper aims to compare the performance of these two evolutionary algorithms in optimizing such problems by minimizing the total cost that the system faces to potential disruption risks. The mechanism and implementation of these two evolutionary algorithms is presented in this paper. Also, using an optimization considers ordering, purchasing, inventory, transportation, and carbon tax cost, a numerical real-life case study is presented to demonstrate the validity of the effectiveness of these algorithms. A comparative study for the algorithms performance has been carried out based on the quality of the obtained solution and the results indicate that the GA performs better than PSO in finding lower-cost solution to the addressed SSCND problem. Despite a lot of research literature being done regarding these two algorithms in solving problems of SCND, few studies have compared the optimization performance between GA and PSO, especially the design of sustainable systems under risk disruptions.
Źródło:: Advances in Science and Technology. Research Journal; 2021, 15, 4; 342-351
2299-8624
Pojawia się w:: Advances in Science and Technology. Research Journal
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 47.

Tytuł:: The influence of inertia weight on the Particle Swarm Optimization algorithm
Autorzy:: Cekus, D.
Skrobek, D.
Powiązania:: https://bibliotekanauki.pl/articles/122644.pdf
Data publikacji:: 2018
Wydawca:: Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
Tematy:: particle swarm optimization (PSO)
PSO algorithm
inertia weight
trajectory
optymalizacja rojem cząstek
PSO
algorytm PSO
metoda PSO
algorytm optymalizacji rojem cząstek
trajektoria
współczynnik wagowy
Opis:: The paper presents the use of the Particle Swarm Optimization (PSO) algorithm to find the shortest trajectory connecting two defined points while avoiding obstacles. The influence of the inertia weight and the number of population adopted in the first iteration of the PSO algorithm was examined for the length of the sought trajectory. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is widely used in literature.
Źródło:: Journal of Applied Mathematics and Computational Mechanics; 2018, 17, 4; 5-11
2299-9965
Pojawia się w:: Journal of Applied Mathematics and Computational Mechanics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 48.

Tytuł:: Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
Autorzy:: He, Ping
Dong, Jie
Wu, Xiaopeng
Yun, Lei
Yang, Hua
Powiązania:: https://bibliotekanauki.pl/articles/27309934.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: BP neural network
photovoltaic power generation
PSO–GWO model
PSO–GWO–BP prediction model
particle swarm optimization
gray wolf optimization
back propagation
standard grey wolf algorithm
Opis:: At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
Źródło:: Archives of Electrical Engineering; 2023, 72, 3; 613--628
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 49.

Tytuł:: An Analytical Study for the Role of Fuzzy Logic in Improving Metaheuristic Optimization Algorithms
Autorzy:: Vij, Sonakshi
Jain, Amita
Tayal, Devendra
Castillo, Oscar
Powiązania:: https://bibliotekanauki.pl/articles/385121.pdf
Data publikacji:: 2018
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: fuzzy logic
metaheuristics
evolutionary computing
genetic algorithm
particle swarm optimization (PSO)
ant colony optimization
fuzzy evolutionary algorithm
fuzzy cuckoo
fuzzy simulated annealing
fuzzy swarm intelligence
fuzzy differential evolution
tabu
fuzzy mutation
fuzzy natural selection
fuzzy fitness function
big bang big crunch
fuzzy bacterial
neuro fuzzy logic
logika rozmyta
metaheurystyka
obliczenia ewolucyjne
algorytm genetyczny
optymalizacja roju cząstek
optymalizacja kolonii mrówek
Opis:: The research applications of fuzzy logic have always been multidisciplinary in nature due to its ability in handling vagueness and imprecision. This paper presents an analytical study in the role of fuzzy logic in the area of metaheuristics using Web of Science (WoS) as the data source. In this case, 178 research papers are extracted from it in the time span of 1989-2016. This paper analyzes various aspects of a research publication in a scientometric manner. The top cited research papers, country wise contribution, topmost organizations, top research areas, top source titles, control terms and WoS categories are analyzed. Also, the top 3 fuzzy evolutionary algorithms are extracted and their top research papers are mentioned along with their topmost research domain. Since neuro fuzzy logic poses feasible options for solving numerous research problems, hence a section is also included by the authors to present an analytical study regarding research in it. Overall, this study helps in evaluating the recent research patterns in the field of fuzzy metaheuristics along with envisioning the future trends for the same. While on one hand this helps in providing a new path to the researchers who are beginners in this field as they can start exploring it through the analysis mentioned here, on the other hand it provides an insight to professional researchers too who can dig a little deeper in this field using knowledge from this study.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 4; 11-27
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 50.

Tytuł:: Identification of the Heat Transfer Coefficient in the Inverse Stefan Problem by Using the ABC Algorithm
Autorzy:: Hetmaniok, E.
Słota, D.
Zielonka, A.
Powiązania:: https://bibliotekanauki.pl/articles/382882.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: solidification process
foundry industry
application of information technology
Stefan problem
swarm intelligence
ABC algorithm
proces krzepnięcia
przemysł odlewniczy
zastosowanie technologii informatycznych
Problem Stefana
inteligencja roju
algorytm ABC
Opis:: A procedure based on the Artificial Bee Colony algorithm for solving the two-phase axisymmetric one-dimensional inverse Stefan problem with the third kind boundary condition is presented in this paper. Solving of the considered problem consists in reconstruction of the function describing the heat transfer coefficient appearing in boundary condition of the third kind in such a way that the reconstructed values of temperature would be as closed as possible to the measurements of temperature given in selected points of the solid. A crucial part of the solution method consists in minimizing some functional which will be executed with the aid of one of the swarm intelligence algorithms - the ABC algorithm.
Źródło:: Archives of Foundry Engineering; 2012, 12, 2s; 27-32
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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