A Cooperative approach to particle swarm optimization

The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO,...

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Vydáno v:	IEEE transactions on evolutionary computation Ročník 8; číslo 3; s. 225 - 239
Hlavní autoři:	van den Bergh, F., Engelbrecht, A.P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.06.2004 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Africa Algorithms Applied sciences Artificial intelligence Benchmarking Computer science Computer science; control theory; systems Exact sciences and technology Genetic algorithms Information technology Mathematical analysis Neural networks Optimization Particle swarm optimization Partitioning algorithms Pattern recognition. Digital image processing. Computational geometry Performance enhancement Space technology Stochastic processes Topology Vectors (mathematics) Convergence behavior Evolutionary algorithm cooperative learning cooperative swarms Neural network particle swarm optimization Coevolution Optimization Stochastic programming Collective learning Algorithm performance Genetic algorithm cooperative coevolutionary genetic algorithm Swarm intelligence Mathematical programming
ISSN:	1089-778X, 1941-0026
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Popis
Shrnutí:	The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using multiple swarms to optimize different components of the solution vector cooperatively. Application of the new PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional PSO.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23
ISSN:	1089-778X 1941-0026
DOI:	10.1109/TEVC.2004.826069