A Hybrid Spherical Evolution and Particle Swarm Optimization Algorithm

A metaheuristic algorithm called spherical evolution (SE) is proposed recently, and the SE innovatively adopts a novel spherical search mechanism instead of the conventional hypercube search mechanism. SE has shown superior performance over other metaheuristic algorithms. However, it still suffers from low search performance and low convergence speed. In this paper, we for the first time propose a hybrid spherical evolution and particle swarm optimization algorithm aimed to leverage strengths of two different search mechanism in a hybrid algorithm, and design a search mechanism control rule based on the fitness of individuals. Experimental results based on 30 benchmark functions of IEEE CEC2017 and results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.