Grey wolf optimizer with cellular topological structure

Abstract

Grey wolf optimizer (GWO) is a newly developed metaheuristic inspired by hunting mechanism of grey wolves. The paramount challenge in GWO is that it is prone to stagnation in local optima. This paper proposes a cellular grey wolf optimizer with a topological structure (CGWO). The proposed CGWO has two characteristics. Firstly, each wolf has its own topological neighbors, and interactions among wolves are restricted to their neighbors, which favors exploitation of CGWO. Secondly, information diffusion mechanism by overlap among neighbors can allow to maintain the population diversity for longer, usually contributing to exploration. Empirical studies are conducted to compare the proposed algorithm with different metaheuristics such as success-history based adaptive differential evolution with linear population size reduction (LSHADE), teaching-learning based optimization algorithm (TLBO), effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR), novel dynamic harmony search (NDHS), bat-inspired algorithm (BA), comprehensive learning particle swarm optimizer (CLPSO), evolutionary algorithm based on decomposition (EAD), ring topology PSO (RPSO), crowding-based differential evolution (CDE), neighborhood based crowding differential evolution (NCDE), locally informed particle swarm (LIPS), some improved variants of GWO and GWO. Experimental results show that the proposed method performs better than the other algorithms on most benchmarks and engineering problems.