Abstract
The basic particle swarm optimisation PSO algorithm is easily trapped in local optima. To deal with this problem, a multi-subpopulation cooperative particle swarm optimisation MCPSO is presented. In the proposed algorithm, the particles are divided into several normal subpopulations and an elite subpopulation. The selected individuals in normal subpopulation are memorised into the elite subpopulation, and some individuals in normal subpopulation are replaced by the best particles from the elite subpopulation. Different subpopulation adopts different evolution model. This strategy can maintain the diversity of the population and avoid the premature convergence. The performance of the proposed algorithm is evaluated by testing on standard benchmark functions. The experimental results show that the proposed algorithm has better convergent rate and high solution accuracy.
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