A modified micro genetic algorithm for undertaking multi-objective optimization problems

Tan, Choo-Jun, Lim, Chee Peng and Cheah, Yu-N. 2013, A modified micro genetic algorithm for undertaking multi-objective optimization problems, Journal of intelligent and fuzzy systems, vol. 24, no. 3, pp. 483-495, doi: 10.3233/IFS-2012-0568.

Attached Files
Name Description MIMEType Size Downloads

Title A modified micro genetic algorithm for undertaking multi-objective optimization problems
Author(s) Tan, Choo-Jun
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Cheah, Yu-N.
Journal name Journal of intelligent and fuzzy systems
Volume number 24
Issue number 3
Start page 483
End page 495
Total pages 13
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2013
ISSN 1064-1246
Keyword(s) multi-objective optimisation
micro genetic algorithm
non-dominated sorting genetic algorithm-II
elitism strategy
population initialisation strategy
Summary 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.
Language eng
DOI 10.3233/IFS-2012-0568
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057604

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 7 times in TR Web of Science
Scopus Citation Count Cited 10 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 252 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Tue, 12 Nov 2013, 13:47:10 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.