Abstract
A memetic algorithm MCLPSO based on the comprehensive learning PSO (CLPSO) is presented in this study. In MCLPSO, a chaotic local search operator is used and a Simulated Annealing (SA) based local search strategy is developed by combining the cognition-only PSO model with SA. The memetic scheme can enable the stagnant particles which cannot be improved by the comprehensive learning strategy to escape from the local optima and enable some elite particles to give fine-grained local search around the promising regions. The experimental result demonstrates a good performance of MCLPSO in optimizing the multimodal functions compared with some other variants of PSO including CLPSO.
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