Evolutionary Many-Objective Optimization Based on Adversarial Decomposition

Mengyuan WU, Ke LI, Sam KWONG, Qingfu ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

77 Citations (Scopus)

Abstract

The decomposition-based evolutionary algorithm has become an increasingly popular choice for posterior multiobjective optimization. Facing the challenges of an increasing number of objectives, many techniques have been developed which help to balance the convergence and diversity. Nevertheless, according to a recent study by Ishibuchi et al., due to the predefined search directions toward the ideal point, their performance strongly depends on the Pareto front (PF) shapes, especially the orientation of the PFs. To balance the convergence and diversity for decomposition-based methods and to alleviate their performance dependence on the orientation of the PFs, this paper develops an adversarial decomposition method for many-objective optimization, which leverages the complementary characteristics of different subproblem formulations within a single paradigm. More specifically, two populations are co-evolved by two subproblem formulations with different contours and adversarial search directions. To avoid allocating redundant computational resources to the same region of the PF, the two populations are matched into one-to-one solution pairs according to their working regions upon the PF. Each solution pair can at most contribute one principal mating parent during the mating selection process. When comparing nine state-of-the-art many-objective optimizers, we have witnessed the competitive performance of our proposed algorithm on 130 many-objective test problems with various characteristics, including regular and inverted PFs.
Original languageEnglish
Pages (from-to)753-764
JournalIEEE Transactions on Cybernetics
Volume50
Issue number2
Early online date19 Oct 2018
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Hong Kong Research Grants Council (RGC) General Research Fund under Grant 9042038 (CityU 11205314), in part by the ANR/RCC Joint Research Scheme through the Hong Kong RGC and the France National Research Agency under Project A-CityU101/16, in part by the Royal Society under Grant IEC/NSFC/170243, and in part by the Chinese National Science Foundation of China under Grant 61672443 and Grant 61473241.

Keywords

  • Adversarial decomposition
  • evolutionary algorithm
  • many-objective optimization
  • stable matching theory

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