Large-scale Partially Separable Function optimization Using Cooperative Coevolution and Competition Strategies

Abstract

Optimizing of the large-scale partially separable functions in the real world is a challenging task. In this paper, we devise a novel optimization method based on coevolution and competition strategies. the proposed method is adopted in two stages: 1) the differential grouping (DG) is used to decompose the original problems into several different subcomponents; 2)Competitive swarm optimizer (CSO) is used to optimize the subcomponents individually. Experimental results show that the combining of DG and CSO performs better than state-of-the-art metaheuristic methods on partially separable functions optimization.