Improved evolutionary algorithms for solving constrained optimization problems with tiny feasible space

Abu S.S.M.Barkat Ullah, Ehab Z. Elfeky, David Cornforth, Daryl L. Essam, Ruhul Sarker

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

3 Citations (Scopus)

Abstract

The quality of individuals in the initial population influences the performance of evolutionary algorithms, especially when the feasible region of the constrained optimization problems is very tiny in comparison to the entire search space. Too much diversity of the population may cost huge processing time; on the other hand the algorithms may trap into local optima for lack of diversity. This paper proposes a simple method to improve the quality of randomly generated initial solutions by sacrificing very little in diversity of the population. We introduce the method of search space reduction technique (SSRT) which is tested using four different existing EAs by solving a number of state-of-the-art test problems and a real world case problem. The experimental results show SSRT improves the solution qualities as well as speeding up the performance of the algorithm.

Original languageEnglish
Title of host publicationProceedings of the 2008 IEEE International Conference on Systems, Man and Cybernetics
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1426-1433
Number of pages8
ISBN (Print)9781424423835
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 12 Oct 200815 Oct 2008

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008
Country/TerritorySingapore
CitySingapore
Period12/10/0815/10/08

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