Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization

Abstract

Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithm are two popular swarm intelligence optimization algorithms and these two algorithms have their own search mechanisms. Based on their unique search mechanisms and their advantages after the improvements on them, this paper proposes a novel hybrid algorithm based on PSO and GWO (Hybrid GWO with PSO, HGWOP). Firstly, GWO is simplified and a novel differential perturbation strategy is embedded in the search process of the simplified GWO to form a Simplified GWO with Differential Perturbation (SDPGWO) so that it can improve the global search ability while retaining the strong exploitation ability of GWO. Secondly, a stochastic mean example learning strategy is applied to PSO to create a Mean Example Learning PSO (MELPSO) to enhance the global search ability of PSO and prevent the algorithm from falling into local optima. Finally, a poor-for-change strategy is proposed to organically integrate SDPGWO and MELPSO to obtain an efficient hybrid algorithm of GWO and PSO. HGWOP can give full play to the advantages of these two improved algorithms, overcome the shortcomings of GWO and PSO and maximize the whole performance. A large number of experiments on the complex functions from CEC2013 and CEC2015 test sets reveal that HGWOP has better optimization performance and stronger universality compared with quite a few state-of-the-art algorithms. Experimental results on K-means clustering optimization show that HGWOP has obvious advantages over the comparison algorithms.