Dynamic HypE for solving single objective optimisation problems

被引：0
作者：
Zhang Q. [1 ]
Zhao F. [2 ]
Zeng S. [3 ]
机构：
[1] School of Computer Science, Huanggang Normal University, Huanggang, Hubei
[2] Science and Technology on Blind Signal Processing Laboratory, Southwest Electronics and Telecommunication Technology Research Institute, Chengdu, Sichuan
[3] School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, Hubei
来源：
International Journal of Innovative Computing and Applications | 2019年 / 10卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Evolutionary algorithms; Multi-objective optimisation problems; Niche count; Single optimisation problems;
D O I：
10.1504/ijica.2019.10022308
中图分类号：
学科分类号：
摘要：
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. Copyright © 2019 Inderscience Enterprises Ltd.
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页码：51 / 58
页数：7
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