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

Guo Yu, Ruimin Shen, Jinhua Zheng, Miqing Li, Juan Zou, Yuan Liu

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
180 Downloads (Pure)

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

Keywords

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

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