Search Bias in Constrained Evolutionary Optimization

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

Abstract

A common approach to constraint handling in evolutionary optimization is to apply a penalty function to bias the search toward a feasible solution. It has been proposed that the subjective setting of various penalty parameters can be avoided using a multiobjective formulation. This paper analyzes and explains in depth why and when the multiobjective approach to constraint handling is expected to work or fail. Furthermore, an improved evolutionary algorithm based on evolution strategies and differential variation is proposed. Extensive experimental studies have been carried out. Our results reveal that the unbiased multiobjective approach to constraint handling may not be as effective as one may have assumed.

Details

Original languageEnglish
Pages (from-to)233-243
Number of pages11
JournalIEEE Transactions on Systems, Man and Cybernetics, Part C
Volume35
Issue number2
Publication statusPublished - 1 May 2005

Keywords

  • penalty functions, nonlinear programming, evolution strategy, multiobjective optimization