Using Grey Wolf Hunting Mechanism to Improve Spherical Search

Abstract

Spherical search algorithm (SS) was a swarm-based meta-heuristic recently proposed to solve the bound-constrained non-linear global optimization problems. It has quite competitive performance with respect to other popular algorithms. Nevertheless, it still has several defects, such as it can’t easily get rid of the situation that falls into the local optimal and its convergence speed is slow under the condition that the spherical space is much too large. As grey wolf optimization (GWO) algorithm has good abilities of minimizing the global search space and local area avoidance, the search mechanism of GWO by serial pattern is studied and combined with SS to improve its balance between exploration and exploitation. The new spherical search and grey wolf optimization algorithm algorithm we proposed is called SSGWO, and its superiority is demonstrated with experimental results based on 30 benchmark functions of IEEE CEC2017 in comparison with its component algorithms.