Integration of metaheuristics: A way to improve search performance

Phen Chiak See, Kuan Yew Wong, Komarudin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

It is generally stated that hybridizing metaheuristics helps to achieve a better search performance when solving combinatorial optimization problems such as Quadratic Assignment Problems (QAPs). In this paper, the integration of two metaheuristics (Max-Min Ant System and Genetic Algorithm) is used to solve a QAP sample, and the search performance obtained is discussed. It is shown that the collaborative effect between these two algorithms helps to search the solution space more effectively, thus achieving a better quality solution.

Original languageEnglish
Title of host publicationComputational Methods in Science and Engineering - Advances in Computational Science, Lectures Presented at the Int. Conference on Computational Methods in Science and Engineering 2008, ICCMSE 2008
Pages868-871
Number of pages4
DOIs
Publication statusPublished - 2009
Event6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008 - Hersonissos, Crete, Greece
Duration: 25 Sep 200830 Sep 2008

Publication series

NameAIP Conference Proceedings
Volume1148 2
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008
Country/TerritoryGreece
CityHersonissos, Crete
Period25/09/0830/09/08

Keywords

  • Genetic Algorithm
  • Integration
  • Max-Min Ant System

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