Abstract
In this paper, an advanced particle swarm optimization algorithm (PSO) is proposed to solve multi-modal function optimization problems. Multiple swarms are used for parallel search, and an artificial repulsive potential field on local search space is set up to prevent multiple swarms converging to the same areas. In addition, this paper provides a theoretical analysis of the strategy of multi-swarm parallel search in algorithms. Finally, the proposed algorithm has been tested on three benchmark functions, and the results show a superior performance compared with other PSO variants.
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