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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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Online Access: | http://eprints.usm.my/29006/1/ENHANCED_MICRO_GENETIC_ALGORITHM-BASED_MODELS_FOR_MULTI-OBJECTIVE_OPTIMIZATION.pdf |
| Summary: | 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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