A ranking-based evolutionary algorithm for constrained optimization problems

Yibo Hu*, Yiu Ming CHEUNG, Yuping Wang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)

Abstract

In constrained optimization problems, evolutionary algorithms often utilize a penalty function to deal with constraints, which is, however, difficult to control the penalty parameters. This paper therefore presents a new constraint handling scheme. It adaptively defines an extended-feasible region that includes not only all feasible solutions, but some infeasible solutions near the boundary of the feasible region. Furthermore, we construct a new fitness function based on stochastic ranking, and meanwhile propose a new crossover operator that can produce more good individuals in general. Accordingly, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations show the efficiency of the proposed algorithm on four benchmark problems.

Original languageEnglish
Title of host publicationProceedings - Third International Conference on Natural Computation, ICNC 2007
Pages198-202
Number of pages5
DOIs
Publication statusPublished - 2007
Event3rd International Conference on Natural Computation, ICNC 2007 - Haikou, Hainan, China
Duration: 24 Aug 200727 Aug 2007

Publication series

NameProceedings - Third International Conference on Natural Computation, ICNC 2007
Volume4

Conference

Conference3rd International Conference on Natural Computation, ICNC 2007
Country/TerritoryChina
CityHaikou, Hainan
Period24/08/0727/08/07

Scopus Subject Areas

  • Applied Mathematics
  • Computational Mathematics
  • Modelling and Simulation

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