Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "particle swarm optimization algorithm" wg kryterium: Temat


Wyświetlanie 1-9 z 9
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
Tytuł:
Optimal design of RC frames using a modified hybrid PSOGSA algorithm
Optymalny projekt ramy RC z wykorzystaniem zmodyfikowanego algorytmu hybrydowego PSOGSA
Autorzy:
Chutani, S.
Singh, J.
Powiązania:
https://bibliotekanauki.pl/articles/230376.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
projekt optymalny
konstrukcja żelbetowa
optymalizacja demokratyczna roju cząstek
samoadaptacyjny algorytm
grawitacyjnego wyszukiwania
projektowanie
Indie
norma indyjska
optimum design
reinforced concrete structure
democratic particle swarm optimization
selfadaptive gravitational search algorithm
design
Indian standard
Opis:
The present study has been taken up to emphasize the role of the hybridization process for optimizing a given reinforced concrete (RC) frame. Although various primary techniques have been hybrid in the past with varying degree of success, the effect of hybridization of enhanced versions of standard optimization techniques has found little attention. The focus of the current study is to see if it is possible to maintain and carry the positive effects of enhanced versions of two different techniques while using their hybrid algorithms. For this purpose, enhanced versions of standard particle swarm optimization (PSO) and a standard gravitational search algorithm (GSA), were considered for optimizing an RC frame. The enhanced version of PSO involves its democratization by considering all good and bad experiences of the particles, whereas the enhanced version of the GSA is made self-adaptive by considering a specific range for certain parameters, like the gravitational constant and a set of agents with the best fitness values. The optimization process, being iterative in nature, has been coded in C++. The analysis and design procedure is based on the specifications of Indian codes. Two distinct advantages of enhanced versions of standard PSO and GSA, namely, better capability to escape from local optima and a faster convergence rate, have been tested for the hybrid algorithm. The entire formulation for optimal cost design of a frame includes the cost of beams and columns. The variables of each element of structural frame have been considered as continuous and rounded off appropriately to consider practical limitations. An example has also been considered to emphasize the validity of this optimum design procedure.
W niniejszym artykule przedstawiono bardziej realistyczny i optymalny projekt żlbetowych ram konstrukcyjnych (RC) poprzez hybrydyzację ulepszonych wersji standardowej optymalizacji roju cząsteczek (PSO) oraz standardowy algorytm wyszukiwania grawitacyjnego (GSA). Podejście proponowane w niniejszej pracy koncentruje się na hybrydyzacji ulepszonych wersji standardowej optymalizacji roju cząsteczek (PSO) oraz standardowym algorytmie wyszukiwania grawitacyjnego (GSA). PSO została zdemokratyzowana poprzez uwzględnienie wszystkich dobrych i złych doświadczeń w zakresie cząsteczek, podczas gdy GSA został zmieniony na samodostosowujący, uwzględniając określony zakres dla niektórych parametrów, takich jak np. stała grawitacyjna i zestaw czynników o najlepszych wartościach sprawności. Optymalny rozmiar i wzmocnienie elementów zostały określone dzięki zastosowaniu techniki w środowisku komputerowym, w którym cały proces analizy, projektowania i optymalizacji został zakodowany w C++. Procedura analizy i projektowania przebiega zgodnie ze specyfikacjami kodów indyjskich. Okazało się, że zastosowanie samodostosowującego algorytmu wyszukiwania grawitacyjnego wraz z demokratyczną techniką optymalizacji roju cząsteczek zapewnia dwie wyraźne przewagi nad standardową PSO i GSA, a mianowicie lepszą zdolność do ucieczki od lokalnej optymalności i szybszy współczynnik konwergencji. Całe sformułowanie dla optymalnego projektu kosztów ramy obejmuje zarówno koszt belek i słupów. W tym podejściu, zmienne każdego elementu ramy konstrukcyjnej zostały uznane za funkcje ciągłe i zaokrąglone odpowiednio do zastosowania praktycznego znaczenia niniejszego badania. Rozważono kilka przykładów, które podkreślają ważność optymalnej procedury projektowania, a wyniki porównano z wcześniejszymi badaniami, w celu sprawdzenia ich skuteczności i efektywności. Proponowany algorytm pokonuje ograniczenia dwóch indywidualnych algorytmów, biorąc pod uwagę ich hybrydę, a tym samym poprawia ogólną wydajność. Wprowadzono niezbędne zmiany, aby badanie było zgodne z wcześniejszymi badaniami. Porównanie z innymi wcześniej stosowanymi technikami hybrydowymi pokazuje, że czas potrzebny na przeprowadzenie procesu optymalizacji w niniejszym badaniu – z wykorzystaniem techniki MPSOGSA – został znacznie zmniejszony. Ponadto, podczas projektowania ram RC obniżono całkowity koszt za pomocą techniki MPSOGSA. Obniżenie kosztów w obszarze stali odgrywa większą rolę w optymalizacji, w porównaniu do redukcji kosztów w przekroju poprzecznym elementów ramy, co zostało szczegółowo przeanalizowane na przykładzie.
Źródło:
Archives of Civil Engineering; 2017, 63, 4; 123-134
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-9 z 9

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies