Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization

Tan, Choo Jun (2014) Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization. PhD thesis, Universiti Sains Malaysia.

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Abstract

Multi-objective Optimization Problems (MOPs) entail multiple conflicting objectives to be satisfied simultaneously. As such, a set of alternative solutions that is able to satisfy all objectives with respect to the Pareto optimality principle is desired. Besides that, the quality of good MOP solutions needs to strike a balance between convergence and diversity against the true Pareto front (i.e. distribution of the ideal Pareto optimal solutions). This research is concerned with how evolutionary algorithms can be employed to undertake MOPs with good convergence and diversity properties of the solutions with respect to the true Pareto front. Masalah pengoptimuman berbilang objektif (Multi-objective Optimization Problem-MOP) melibatkan berbilang objektif yang perlu dipenuhi serentak. Sekumpulan penyelesaian optimuman alternatif diperlukan untuk memenuhi kesemua objektif yang menunju ke arah barisan Pareto.

Item Type: Thesis (PhD)
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User: Administrator Automasi
Date Deposited: 08 Jun 2015 07:29
Last Modified: 12 Apr 2019 05:26
URI: http://eprints.usm.my/id/eprint/29006

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