Multi-leader PSO (MLPSO): A new PSO variant for solving global optimization problems

Abstract

Particle swarm optimization (PSO) has long been attracting wide attention from researchers in the community. How to deal with the weak exploration ability and premature convergence of PSO remains an open question. In this paper, we modify the memory structure of canonical PSO and introduce the multi-leader mechanism to alleviate these problems. The proposed PSO variant in this paper is termed as multi-leader PSO (MLPSO) within which the modified memory structure provided more valuable information for particles to escape from the local optimum and multi-leader mechanism enhances diversity of particles' search pattern. Under the multi-leader mechanism, particles choose their leaders based on the game theory instead of a random selection. Besides, the best leader refers to other leaders' information to improve its quality in every generation based on a self-learning process. To make a comprehensive analysis, we test MLPSO against the benchmark functions in CEC 2013 and further applied MLPSO to a practical case: the reconstruction of gene regulatory networks based on fuzzy cognitive maps. The experimental results confirm that MLPSO enhances the efficiency of the canonical PSO and performs well in the realistic optimization problem.