Enhanced multi-swarm cooperative particle swarm optimizer

In this paper, a novel multi-swarm particle swarm optimizer driven by delayed-activation (DA) strategy and repulsive mechanism, named as enhanced multi-swarm cooperative particle swarm optimizer (EMCPSO) is proposed. EMCPSO is designed to make use of the advantage of multi-swarm technique and overcome the problem of premature convergence of original PSO. In this algorithm, the whole population is partitioned into four identical sub-swarms. The best particle of each sub-swarm, sbest, is used to estimate the evolutionary state of the group. If the sbest can continuously improve its solution's quality, that sub-swarm evolves independently without communicating with other counterparts. Otherwise, based on a non-ascending sequence, a delayed-activation (DA) strategy will be triggered. With information sharing among multi-swarm, activating exemplar is constructed to promote the stagnant sub-swarm to search for better solutions again. On the other hand, a repulsive mechanism is introduced to prevent the whole population from gathering together prematurely. In this way, more potential regions of the search space can be explored by EMCPSO. The experiment results on CEC 2017 problem set demonstrate the superior performance of the proposed EMCPSO in terms of solution accuracy and convergence speed.

Automated summary

This paper proposes an enhanced multi-swarm cooperative particle swarm optimizer (EMCPSO) that utilizes the advantage of multi-swarm technique and solves the problem of premature convergence of the original PSO. It introduces a delayed-activation (DA) strategy and a repulsive mechanism to promote solution quality and prevent premature convergence. Experimental results on CEC 2017 problem set demonstrate the superior performance of the proposed EMCPSO in terms of solution accuracy and convergence speed.