A memetic particle swarm optimization algorithm for multimodal optimization problems

In this paper, a new memetic algorithm, which combines PSO and local search technique, is proposed for mul-timodal optimization problems. In the investigated algorithm, a local PSO model is used to disperse the individuals into different sub-regions, an adaptive local search method is employed to refine the quality of individuals and a triggered re-initialization scheme is introduced to enhance the algorithm's capacity of solving functions with numerous optima. Experimental results based on a set of benchmark functions show that the proposed memetic algorithm is a good optimizer in multimodal optimization domain.

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