Authors: Qing Zhang; Fei Zhao; Sanyou Zeng
Addresses: School of Computer Science, Huanggang Normal University, 438000 Huanggang, Hubei, China ' Science and Technology on Blind Signal Processing Laboratory, Southwest Electronics and Telecommunication Technology Research Institute, 610041 Chengdu, Sichuan, China ' School of Mechanical Engineering and Electronic Information, China University of Geosciences, 430074 Wuhan, Hubei, China
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.
Keywords: evolutionary algorithms; single optimisation problems; multi-objective optimisation problems; niche count.
International Journal of Innovative Computing and Applications, 2019 Vol.10 No.1, pp.51 - 58
Received: 08 Sep 2018
Accepted: 19 Mar 2019
Published online: 28 Jun 2019 *