Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 8, Issue 3, Pages 225-239Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/tevc.2004.826069
Keywords
convergence behavior; cooperative coevolutionary genetic algorithm; cooperative learning; cooperative swarms; particle swarm optimization
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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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