Improved catfish particle swarm optimization with embedded chaotic map

Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The introduced catfish particles improve the performance of PSO considerably. Unlike other ordinary particles, catfish particles initialize a new search from extreme points of the search space when the gbest fitness value (the best previously encountered value) has not changed for a certain number of consecutive iterations. This results in further opportunities of finding better solutions for the swarm by guiding the entire swarm to promising new regions of the search space, and by accelerating search efficiency. In this study, we adopted chaotic maps to strengthen the solution quality of PSO and CatfishPSO. After the introduction of chaotic maps into the process, the improved PSO and CatfishPSO are called chaotic PSO (C-PSO) and chaotic CatfishPSO (C-CatfishPSO), respectively. PSO, C-PSO, CatfishPSO and C-CatfishPSO were extensively compared on six benchmark functions. Statistical analysis of the experimental results indicates that the performance of C-CatfishPSO is better than the performance of PSO, C-PSO, and CatfishPSO.