Binary search based boundary elimination selection in many-objective evolutionary optimization

Research output: Contribution to journalArticle

Authors

  • Guo Yu
  • Ruimin Shen
  • Jinhua Zheng
  • Juan Zou
  • Yuan Liu

Colleges, School and Institutes

External organisations

  • School of Information Engineering, Xiangtan University
  • Department of Computer Sciences, University of Surrey
  • School of Computer Science and Technology, Hengyang Normal University

Abstract

In many-objective optimization, the balance between convergence and diversity is hard to maintain, while the dominance resistant solutions (DRSs) could further harm the balance particularly in high-dimensional objective space. Thus, this paper proposes a novel selection strategy – boundary elimination selection based on binary search (called BESBS), trying to avoid the impact of DRSs during the optimization and achieve a good balance between the convergence and diversity simultaneously. During the environmental selection, the binary search (BS) is used to adaptively adjust the ϵ value in the ϵ-dominance relationship and assist in detecting the well-distributed neighbors for the elite solutions. Then the ϵ value obtained by BS is used for serving the boundary elimination selection (BES) to guarantee the stability of the elite population. To improve the convergence, BES is mainly designed to select individuals approximating to the ideal point. By modifying the fitness of solutions and choosing solutions in terms of the shuffled sequence of objective axis, the DRSs will be eliminated during the selection. Thus, BESBS could achieve a good balance between the convergence and diversity and avoid the impact from DRSs simultaneously. From a series of experiments with 35 instances, the experimental results have shown that BESBS is competitive against 8 state-of-art many-objective evolutionary algorithms.

Details

Original languageEnglish
Pages (from-to)689-705
JournalApplied Soft Computing
Volume60
Early online date21 Jul 2017
Publication statusPublished - 1 Nov 2017

Keywords

  • many-objective optimization problems , boundary elimination selection , binary search