A Population Resource Allocation-based Adaptive Spherical Search Algorithm

The spherical search (SS) algorithm is a meta-heuristic algorithm which aims to solve the global optimization problems that is non-linear and bound-constrained. Due to the balance between the exploration and exploitation capabilities of the algorithm by utilizing two mechanisms, namely, toward-random and toward-best, the SS algorithm shows superior performance compared to some other meta-heuristic techniques. However, the algorithm still suffers from the drawback of being easily trapped in local optima. In this paper, we propose a population resource allocation-based adaptive spherical search algorithm that adaptively adjusts the ratio of population resource allocation. The algorithm evaluates the performance of different operators at the current search stage in real-time during the iteration, and continuously adjusts the resource allocation ratio based on the performance until the end of the iteration. Experimental results are obtained on the basis of IEEE Congress on Evolutionary Computation (CEC) 2017 problem set to verify the effectiveness and the efficiency of the proposed method.