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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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 8; no. 3; pp. 225 - 239
Main Authors: van den Bergh, F., Engelbrecht, A.P.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.06.2004
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2004.826069