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Tytuł:
Swarm intelligence for network routing optimization
Autorzy:
Dempsey, P.
Schuster, A.
Powiązania:
https://bibliotekanauki.pl/articles/309012.pdf
Data publikacji:
2005
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
network routing
swarm intelligence
ant algorithms
Opis:
This paper presents the results of a comparative study of network routing approaches. Recent advances in the field suggest that swarm intelligence may offer a robust, high quality solution. The overall aim of the study was to develop a framework to facilitate the empirical evaluation of a swarm intelligence routing approach compared to a conventional static and dynamic routing approach. This paper presents a framework for the simulation of computer networks, collection of performance statistics, generation and reuse of network topologies and traffic patterns.
Źródło:
Journal of Telecommunications and Information Technology; 2005, 3; 24-28
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Swarm intelligence approach to safe ship control
Autorzy:
Lazarowska, A.
Powiązania:
https://bibliotekanauki.pl/articles/258674.pdf
Data publikacji:
2015
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
ant colony optimization
collision avoidance
computer simulation
marine transport
path planning
safe ship control
safety at sea
swarm intelligence
Opis:
This paper presents an application of the Ant Colony Optimization (ACO) technique in a safe ship control system. The method developed solves the problem of path planning and collision avoidance of a ship in the open sea as well as in restricted waters. The structure of the developed safe ship control system is introduced, followed by a presentation of the applied algorithm. Results showing the problem-solving capability of the system are also included. The aim of the system developed is to increase automation of a safe ship control process. It is possible to apply the proposed method in Unmanned Surface Vehicles (USVs) control system, what will contribute to the enhancement of their autonomy.
Źródło:
Polish Maritime Research; 2015, 4; 33-40
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Is Swarm Intelligence Able to Create Mazes?
Autorzy:
Połap, D.
Woźniak, M.
Napoli, C.
Tramontana, E.
Powiązania:
https://bibliotekanauki.pl/articles/226726.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
computational intelligence
heuristic algorithm
Opis:
In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented. For two different algorithms the proposed idea has been examined. The results of the experiments are shown and discussed to present advantages and disadvantages.
Źródło:
International Journal of Electronics and Telecommunications; 2015, 61, 4; 305-310
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fixing Design Inconsistencies of Polymorphic Methods Using Swarm Intelligence
Autorzy:
George, Renu
Samuel, Philip
Powiązania:
https://bibliotekanauki.pl/articles/1818478.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
UML models
software design inconsistency
polymorphism
particle swarm optimization
Opis:
Background: Modern industry is heavily dependent on software. The complexity of designing and developing software is a serious engineering issue. With the growing size of software systems and increase in complexity, inconsistencies arise in software design and intelligent techniques are required to detect and fix inconsistencies. Aim: Current industrial practice of manually detecting inconsistencies is time consuming, error prone and incomplete. Inconsistencies arising as a result of polymorphic object interactions are hard to trace. We propose an approach to detect and fix inconsistencies in polymorphic method invocations in sequence models. Method: A novel intelligent approach based on self regulating particle swarm optimization to solve the inconsistency during software system design is presented. Inconsistency handling is modelled as an optimization problem that uses a maximizing fitness function. The proposed approach also identifies the changes required in the design diagrams to fix the inconsistencies. Result: The method is evaluated on different software design models involving static and dynamic polymorphism and inconsistencies are detected and resolved. Conclusion: Ensuring consistency of design is highly essential to develop quality software and solves a major design issue for practitioners. In addition, our approach helps to reduce the time and cost of developing software.
Źródło:
e-Informatica Software Engineering Journal; 2021, 15, 1; 7--27
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Swarm Intelligence-based Partitioned Recovery in Wireless Sensor Networks
Autorzy:
Kumar, G.
Ranga, V.
Powiązania:
https://bibliotekanauki.pl/articles/307759.pdf
Data publikacji:
2018
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
connectivity restoration
meta-heuristics
relay node placement
wireless sensor networks
Opis:
The failure rate of sensor nodes in Heterogeneous Wireless Sensor Networks is high due to the use of low battery-powered sensor nodes in a hostile environment. Networks of this kind become non-operational and turn into disjoint segmented networks due to large-scale failures of sensor nodes. This may require the placement of additional highpower relay nodes. In this paper, we propose a network partition recovery solution called Grey Wolf, which is an optimizer algorithm for repairing segmented heterogeneous wireless sensor networks. The proposed solution provides not only strong bi-connectivity in the damaged area, but also distributes traffic load among the multiple deployed nodes to enhance the repaired network’s lifetime. The experiment results show that the Grey Wolf algorithm offers a considerable performance advantage over other state-of-the-art approaches.
Źródło:
Journal of Telecommunications and Information Technology; 2018, 3; 70-81
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Swarm intelligence algorithm based on competitive predators with dynamic virtual teams
Autorzy:
Yang, S.
Sato, Y.
Powiązania:
https://bibliotekanauki.pl/articles/91592.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
swarm intelligence
sitness predator optimizer
dynamic virtual team
population diversity
Opis:
In our previous work, Fitness Predator Optimizer (FPO) is proposed to avoid premature convergence for multimodal problems. In FPO, all of the particles are seen as predators. Only the competitive, powerful predator that are selected as an elite could achieve the limited opportunity to update. The elite generation with roulette wheel selection could increase individual independence and reduce rapid social collaboration. Experimental results show that FPO is able to provide excellent performance of global exploration and local minima avoidance simultaneously. However, to the higher dimensionality of multimodal problem, the slow convergence speed becomes the bottleneck of FPO. A dynamic team model is utilized in FPO, named DFPO to accelerate the early convergence rate. In this paper, DFPO is more precisely described and its variant, DFPO-r is proposed to improve the performance of DFPO. A method of team size selection is proposed in DFPO-r to increase population diversity. The population diversity is one of the most important factors that determines the performance of the optimization algorithm. A higher degree of population diversity is able to help DFPO-r alleviate a premature convergence. The strategy of selection is to choose team size according to the higher degree of population diversity. Ten well-known multimodal benchmark functions are used to evaluate the solution capability of DFPO and DFPO-r. Six benchmark functions are extensively set to 100 dimensions to investigate the performance of DFPO and DFPO-r compared with LBest PSO, Dolphin Partner Optimization and FPO. Experimental results show that both DFPO and DFPO-r could demonstrate the desirable performance. Furthermore, DFPO-r shows better robustness performance compared with DFPO in experimental study.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 2; 87-101
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
Autorzy:
Li, C.
Chiang, T. W.
Powiązania:
https://bibliotekanauki.pl/articles/331280.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
zbiór rozmyty
system neuronowo-rozmyty
optymalizacja rojem cząstek
szereg czasowy
complex fuzzy set
complex neuro fuzzy system
hierarchical multi swarm
particle swarm optimization (PSO)
recursive least squares estimator
time series forecasting
Opis:
Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the well known Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 787-800
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Swarm intelligence integrated approach for experimental investigation in milling of multiwall carbon nanotube/polymer nanocomposites
Autorzy:
Kharwar, Prakhar Kumar
Verma, Rajesh Kumar
Mandal, Nirmal Kumar
Mondal, Arpan Kumar
Powiązania:
https://bibliotekanauki.pl/articles/139582.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
nanocomposites
epoxy
particle
swarm
Pareto front
Opis:
In manufacturing industries, the selection of machine parameters is a very complicated task in a time-bound manner. The process parameters play a primary role in confirming the quality, low cost of manufacturing, high productivity, and provide the source for sustainable machining. This paper explores the milling behavior of MWCNT/epoxy nanocomposites to attain the parametric conditions having lower surface roughness (Ra) and higher materials removal rate (MRR). Milling is considered as an indispensable process employed to acquire highly accurate and precise slots. Particle swarm optimization (PSO) is very trendy among the nature-stimulated metaheuristic method used for the optimization of varying constraints. This article uses the non-dominated PSO algorithm to optimize the milling parameters, namely, MWCNT weight% (Wt.), spindle speed (N), feed rate (F), and depth of cut (D). The first setting confirmatory test demonstrates the value of Ra and MRR that are found as 1.62 µm and 5.69 mm3/min, respectively and for the second set, the obtained values of Ra and MRR are 3.74 µm and 22.83 mm3/min respectively. The Pareto set allows the manufacturer to determine the optimal setting depending on their application need. The outcomes of the proposed algorithm offer new criteria to control the milling parameters for high efficiency.
Źródło:
Archive of Mechanical Engineering; 2020, LXVII, 3; 353-376
0004-0738
Pojawia się w:
Archive of Mechanical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence
Autorzy:
Jagtap, A. M.
Powiązania:
https://bibliotekanauki.pl/articles/201287.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
sensor
target
Voronoi
heuristic
ABC
PSO
VABC
czujnik
cel
heurystyka
Opis:
This paper addresses the major challenges that reside on target coverage problem, which is one among the two primary sub-problems of node deployment problem. In order to accomplish a cost-efficient target coverage, a Voronoi partition-based, velocity added artificial bee colony algorithm (V-VABC) is introduced. The V-VABC is an advancement over the traditional, target-based Voronoi greedy algorithm (TVgreedy). Moreover, the VABC component of V-VABC is a hybrid, heuristic search algorithm developed from the context of ABC and particle swarm optimization (PSO). The V-VABC is an attempt to solve the network, which has an equal number of both sensors and targets, which is a special case of TCOV. Simulation results show that V-VABC performs better than TV-greedy and the classical and base algorithms of V-VABC such as ABC and PSO.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2017, 65, 2; 263-272
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie algorytmów rojowych do optymalizacji parametrów w modelach układów regulacji
Application of swarm intelligence algorithms to optimization of control system models
Autorzy:
Tomera, M.
Powiązania:
https://bibliotekanauki.pl/articles/269153.pdf
Data publikacji:
2015
Wydawca:
Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
Tematy:
algorytmy rojowe
optymalizacja parametrów
algorytm mrówkowy
algorytm sztucznej kolonii pszczół
algorytm optymalizacji rojem cząstek
swarm intelligence
swarm based optimization
ant colony optimization
Artificial Bee Colony
particle swarm optimization (PSO)
Opis:
W pracy przedstawione zostały algorytmy rojowe, takie jak: algorytm mrówkowy, zmodyfikowany algorytm mrówkowy, algorytm sztucznej kolonii pszczół oraz algorytm optymalizacji rojem cząstek. Dla tych algorytmów przygotowane zostało oprogramowanie w Matlabie, pozwalające na optymalizację parametrów poszukiwanych modeli matematycznych, wyznaczanych na podstawie przeprowadzonych testów identyfikacyjnych lub na optymalizację parametrów regulatorów zastosowanych w modelach matematycznych układów sterowania.
The paper presents the 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). Ant colony optimization (ACO) based upon the observation of the behavior of ant colonies looking for food in the surrounding anthill. Feeding ants it is based on finding the shortest path transitions between a food source and the anthill. In the process of foraging ants on their paths crossing from the nest to a food source and back, they leave a pheromone trail. The work presents also the modified ant colony algorithm (MACO). This algorithm is based on searching the solution space surrounded by the best solution obtained in the previous iteration. If you find a local minimum, the proposed algorithm uses pheromone to find a new solution space, while retaining the position information current local minimum. The artificial bee colony algorithm is one of the well-known swarm intelligence algorithms. In the past decade there has been created several different algorithms based on the observation of the behavior of cooperative bees. Among them, the most frequently analyzed and used is bee algorithm proposed in 2005 by Dervis Karaboga and was be used in the proposed paper. The particle swarm optimization algorithm (PSO) is based on adjusting the change speed of the moving particles to a speed of particles movement in the neighborhood. Particle optimization algorithm is one of the computational techniques derived on the basis of swarm behavior such as flocks of birds and schools of fish, which is the basis for the functioning of the exchange of information to enable them to cooperate. It was noticed that the animals in the herd tend to maintain the optimum distance from their neighbors, by appropriate adjustment of their speed. This method allows the synchronous and collision-free motion, often accompanied by sudden changes of direction and due to the rearrangement of the optimal formation. For these algorithms has been prepared the software in Matlab, allowing to optimization of the mathematical models designated on the basis of the carried out identification tests and control parameters used in the mathematical model of the control system.
Źródło:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2015, 46; 97-102
1425-5766
2353-1290
Pojawia się w:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applications of genetic algorithm and swarm intelligence algorithms to short transfer
Zastosowanie algorytmu genetycznego oraz algorytmów rojowych do estymacji współczynnika wymiany ciepła
Autorzy:
Raszkowski, Tomasz
Samson, Agnieszka
Powiązania:
https://bibliotekanauki.pl/articles/699848.pdf
Data publikacji:
2017
Wydawca:
Łódzkie Towarzystwo Naukowe
Źródło:
Bulletin de la Société des Sciences et des Lettres de Łódź, Série: Recherches sur les déformations; 2017, 67, 3; 103-125
1895-7838
2450-9329
Pojawia się w:
Bulletin de la Société des Sciences et des Lettres de Łódź, Série: Recherches sur les déformations
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
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
Artykuł
Tytuł:
Improvements to Glowworm Swarm Optimization algorithm
Ulepszenia algorytmu Glowworm Swarm Optimization
Autorzy:
Oramus, P.
Powiązania:
https://bibliotekanauki.pl/articles/305567.pdf
Data publikacji:
2010
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
inteligencja roju
optymalizacja
swarm intelligence
glowworm swarm optimization
multimodal function optimization
Opis:
Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multiple optima of multimodal functions. The algorithm uses an ensemble of agents, which scan the search space and exchange information concerning a fitness of their current position. The fitness is represented by a level of a luminescent quantity called luciferin. An agent moves in direction of randomly chosen neighbour, which broadcasts higher value of the luciferin. Unfortunately, in the absence of neighbours, the agent does not move at all. This is an unwelcome feature, because it diminishes the performance of the algorithm. Additionally, in the case of parallel processing, this feature can lead to unbalanced loads. This paper presents simple modifications of the original algorithm, which improve performance of the algorithm by limiting situations, in which the agent cannot move. The paper provides results of comparison of an original and modified algorithms calculated for several multimodal test functions.
Algorytm Glowworm Swarm Optimization jest stosowany do równoczesnego odnajdywania wielu optimów funkcji multimodalnych. Algorytm używa zespołu agentów przeszukujących przestrzeń poszukiwań i wymieniających się informacjami o wartości funkcji przystosowania w danym położeniu. Funkcja przystosowania jest reprezentowana przez poziom emitującego światło pigmentu - lucyferyny. Agenci poruszają się w kierunku losowo wybranego sąsiada, który rozgłasza wyższą wartość poziomu lucyferyny. Niestety w przypadku braku sąsiadów agent nie porusza się wcale. Stanowi to niepożądaną cechę algorytmu ograniczającą jego wydajność. W przypadku przetwarzania równoległego cecha ta może prowadzić do niezrównoważenia obciążenia. Praca ta przedstawia proste modyfikacje oryginalnego algorytmu zwiększające jego wydajność poprzez ograniczanie liczby takich sytuacji, w których agent nie może się poruszyć. Przedstawione zostały wyniki porównania pracy oryginalnego i zmodyfikowanych algorytmów dla kilku funkcji testowych.
Źródło:
Computer Science; 2010, 11; 7-20
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
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