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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 Cheah
In 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 systems

Volume

24

Issue

3

Pagination

483 - 495

Publisher

IOS Press

Location

Amsterdam, The Netherlands

ISSN

1064-1246

eISSN

1875-8967

Language

eng

Publication classification

C1 Refereed article in a scholarly journal