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A modified micro genetic algorithm for undertaking multi-objective optimization problems
journal contribution
posted on 2013-01-01, 00:00 authored by C J Tan, Chee Peng LimChee Peng Lim, Y N CheahIn this paper, a Modified micro Genetic Algorithm (MmGA) is proposed for undertaking Multi-objective Optimization Problems (MOPs). An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro Genetic Algorithm (mGA) to form the proposed MmGA. The main aim of the MmGA is to improve its convergence rate towards the pareto optimal solutions. To evaluate the effectiveness of the MmGA, two experiments using the Kursawe test function in MOPs are conducted, and the results are compared with those from other approaches using a multi-objective evolutionary algorithm indicator, i.e. the Generational Distance (GD). The outcomes positively demonstrate that the MmGA is able to provide useful solutions with improved GD measures for tackling MOPs.
History
Journal
Journal of intelligent and fuzzy systemsVolume
24Issue
3Pagination
483 - 495Publisher
IOS PressLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
1064-1246eISSN
1875-8967Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Keywords
multi-objective optimisationmicro genetic algorithmnon-dominated sorting genetic algorithm-IIelitism strategypopulation initialisation strategyScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer ScienceMICROGENETIC-ALGORITHMEVOLUTIONARY ALGORITHMSOBJECTIVESOPERATORSBOOTSTRAPSEARCHDESIGNArtificial Intelligence and Image Processing
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