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

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 solu...

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Main Author: Tan, Choo Jun
Format: Thesis
Language:English
Published: 2014
Subjects:
Online Access:http://eprints.usm.my/29006/1/ENHANCED_MICRO_GENETIC_ALGORITHM-BASED_MODELS_FOR_MULTI-OBJECTIVE_OPTIMIZATION.pdf
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author Tan, Choo Jun
author_facet Tan, Choo Jun
author_sort Tan, Choo Jun
collection USM
description 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.
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spelling usm.eprints-290062019-04-12T05:26:08Z http://eprints.usm.my/29006/ Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization Tan, Choo Jun QA75.5-76.95 Electronic computers. Computer science 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. 2014 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/29006/1/ENHANCED_MICRO_GENETIC_ALGORITHM-BASED_MODELS_FOR_MULTI-OBJECTIVE_OPTIMIZATION.pdf Tan, Choo Jun (2014) Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Tan, Choo Jun
Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization
title Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization
title_full Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization
title_fullStr Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization
title_full_unstemmed Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization
title_short Enhanced Micro Genetic Algorithm-Based Models For Multi-Objective Optimization
title_sort enhanced micro genetic algorithm based models for multi objective optimization
topic QA75.5-76.95 Electronic computers. Computer science
url http://eprints.usm.my/29006/1/ENHANCED_MICRO_GENETIC_ALGORITHM-BASED_MODELS_FOR_MULTI-OBJECTIVE_OPTIMIZATION.pdf
work_keys_str_mv AT tanchoojun enhancedmicrogeneticalgorithmbasedmodelsformultiobjectiveoptimization