Dynamic HypE for solving single objective optimisation problems

Abstract

One difficulty in solving optimisation problems is the handling many local optima. The usual approaches to handle the difficulty are to introduce the niche-count into evolutionary algorithms (EAs) to increase population diversity. In this paper, we introduce the niche-count into the problems, not into the EAs. We construct a dynamic multi-objective optimisation problem (DMOP) for the single optimisation problem (SOP) and ensure both the DMOP and the SOP are equivalent to each other. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the local optima difficulty during the search process. A dynamic version of a multi-objective evolutionary algorithm (DMOEA), specifically, HypE-DE, is used to solve the DMOP; consequently the SOP is solved. Experimental results show that the performance of the proposed method is significantly better than the state-of-the-art competitors on a set of test problems.