A modified particle swarm optimization for global optimization

This paper presents a modified optimization algorithm called catfish particle swarm optimization (CatfishPSO), in which the so-called catfish effect is applied to improve the performance of particle swarm optimization (PSO). This effect is the result of the introduction of new particles at extreme points in the search space ("catfish particles"), which replace particles with the worst fitness when the fitness of the global best particle has not improved for a number of consecutive iterations. This results in further opportunities of finding better solutions for the swarm by guiding the whole swarm to promising new regions of the search space. In our experiment, CatfishPSO and other improved PSO procedures were extensively compared on sixteen benchmark functions with variant dimensions. Experimental results indicate that CatfishPSO is easy to implement and achieves better performance than other improved PSO algorithms from the literature.