Empirical study on rotation and information exchange in particle swarm optimization

Abstract This paper investigates whether rotational variance and information exchange affect the performance of Particle Swarm Optimization (PSO) algorithms. Four PSO versions which include the presence or absence of rotational variance, and the fast or late information exchange among particles are under evaluation. The goal is to highlight the best approach and principal benefits of each PSO version based on numerical simulations. To accomplish the aforesaid, the algorithms were evaluated on CEC 2017 benchmark optimization problems. Additionally, a method to estimate a reliable number of algorithms executions was also proposed. Statistical measurements based on Clerc's rules were used to strengthen the analyses. The results indicated the rotationally variant PSO as the overall winner, and the fast information exchange was statistically significant better than the late one, according to Friedman's and Wilcoxon's tests.

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