Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems

Hemant Kumar SINGH, Amitay ISAACS, Trung Thanh NGUYEN, Tapabrata RAY, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

57 Citations (Scopus)

Abstract

A number of population based optimization algorithms have been proposed in recent years to solve unconstrained and constrained single and multi-objective optimization problems. Most of such algorithms inherently prefer a feasible solution over an infeasible one during the course of search, which translates to approaching the constraint boundary from the feasible side of the search space. Previous studies [1], [2] have already demonstrated the benefits of explicitly maintaining a fraction of infeasible solutions in Infeasiblity Driven Evolutionary Algorithm (IDEA) for single and multiobjective constrained optimization problems. In this paper, the benefits of IDEA as a sub-evolve mechanism are highlighted for dynamic, constrained single objective optimization problems. IDEA is particularly attractive for such problems as it offers a faster rate of convergence over a conventional EA, which is of significant interest in dynamic optimization problems. The algorithm is tested on two new dynamic constrained test problems. For both the problems, the performance of IDEA isfound to be significantly better than conventional EA. © 2009 IEEE.
Original languageEnglish
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages3127-3134
Number of pages8
DOIs
Publication statusPublished - May 2009
Externally publishedYes

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