Many-objective Evolutionary Optimization by Self-Controlling Dominance Area of Solutions :

DOI

Bibliographic Information

Other Title
  • 解の支配領域の自己制御による進化型多数目的最適化:
  • 多数目的 0/1 ナップザック問題における性能検証と挙動解析
  • Performance Verification and Analysis in Many-objective 0/1 Knapsack Problems

Abstract

Controlling dominance area of solutions (CDAS) relaxes the concepts of Pareto dominance with an user defined parameter S. This method enhances the search performance of dominance-based MOEA in many-objective optimization problems (MaOPs). However, to bring out desirable search performance, we have to experimentally find out S that controls dominance areas appropriately. Also, there is a tendency to deteriorate the diversity of solutions obtained by CDAS when we decrease S from 0.5. To solve these problems, in this work, we propose a modification of CDAS called self-controlling dominance area of solutions (S-CDAS). In S-CDAS, the algorithm self-controls dominance areas for each solution without the need of an external parameter. S-CDAS considers convergence and diversity and realizes a fine grained ranking that is different from conventional CDAS. In this work, we focus on combinatorial optimization and use many-objective 0/1 knapsack problems with m = 4∼10 objectives to verify the search performance of the proposed method. Simulation results show that S-CDAS achieves well-balanced search performance on both convergence and diversity compared to conventional NSGA-II, CDAS, IBEAε+ and MSOPS. In addition, the algorithm's behavior of S-CDAS is analyzed and discussed.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390282680342249984
  • NII Article ID
    130004965133
  • DOI
    10.11394/tjpnsec.1.32
  • ISSN
    21857385
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

Report a problem

Back to top