Catfish particle swarm optimization

Abstract

Catfish particle swarm optimization (CatfishPSO) is a novel optimization algorithm proposed in this paper. The mechanism is dependent on the incorporation of a catfish particle into the linearly decreasing weight particle swarm optimization (LDWPSO). The introduced catfish particle improves the performance of LDWPSO. Unlike other ordinary particles, the catfish particles will initialize a new search from the extreme points of the search space when the gbest fitness value (global optimum at each iteration) has not been changed for a given time, which results in further opportunities to find better solutions for the swarm by guiding the whole swarm to promising new regions of the search space, and accelerating convergence. In our experiment, CatfishPSO, LDWPSO and other improved PSO procedures were extensively compared on three benchmark test functions with 10, 20 and 30 different dimensions. Experimental results indicate that CatfishPSO achieves better performance than LDWPSO procedure and other improved PSO algorithms from the literature.