A Hybrid Hypercube and Spherical Evolution for Optimization
Zonghui Cai, Yang Yu, Xiao Yang, Hongwei Dai, Shangce Gao · 2019
In recent two decades, nature-inspired metaheuristic algorithms have been paid more and more attention. Although many new algorithms have been proposed, there is no quintessential difference among classic metaheuristic algorithms. Therefore, some researchers have focused on the essence of search operators in these optimizers. Spherical Evolution (SE) is one of the recent studies on the search style in metaheuristic algorithms. Contrary to other algorithms, SE adopts a spherical search instead of a hypercube search. In this paper, we focus on the advantages of the two search styles, We propose a hybrid optimizer based on the two complementary search styles, and design a rule to control their utilization. Experimental results based on 30 benchmark functions of CEC2017 show that the proposed optimizer outperforms other state-of-the-art algorithms in terms of effectiveness and robustness.