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 2004-06, Vol.8 (3), p.225-239
Main Authors: van den Bergh, F., Engelbrecht, A.P.
Format: Article
Language:English
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)
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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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2004.826069