Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

7 Scopus Citations
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Author(s)

  • Ye Tian
  • Yajie Zhang
  • Yansen Su
  • Xingyi Zhang
  • Yaochu Jin

Related Research Unit(s)

Detail(s)

Original languageEnglish
Journal / PublicationIEEE Transactions on Cybernetics
Online published17 Mar 2021
Publication statusOnline published - 17 Mar 2021

Abstract

Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-the-art algorithms, especially on problems with complex feasible regions.

Research Area(s)

  • Constrained multiobjective optimization problems (CMOPs), constraint satisfaction, Convergence, evolutionary algorithm, Evolutionary computation, objective optimization, Optimization, Search problems, Sociology, Sorting, Statistics