Beletew, Mekasha (2014) Genetic Algorithm Applied on Multi-objective Optimization. Masters thesis, Addis Ababa University.
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Abstract
Multi-objective formulations are a realistic models for many complex optimization problems. In this project we presented multiobjective optimization problems using genetic algorithms developed specifically for the problems with multiple objectives. Customized genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions(pareto-optimal points) to the problems. Moreover, in solving multi-objective problems, designers may be interested in a set of pareto-optimal points instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multi-objective optimization problems to capture a number of solutions simultaneously. In this project we also describe the working principle of a binary-coded and real-parameter genetic algorithm, which is ideally suited to handle problems with a continuous search space.Moreover, a non-dominated sorting-based multi-objective evolutionary algorithm (MOEA), called non-dominated sorting genetic algorithm II (NSGA-II), is also presented.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Generic Algorithm, Multi-objective Optimization, Elitism, Pareto optimal solutions, Ordering relation. |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
| Divisions: | Africana |
| Depositing User: | Selom Ghislain |
| Date Deposited: | 11 Sep 2018 12:07 |
| Last Modified: | 11 Sep 2018 12:07 |
| URI: | http://thesisbank.jhia.ac.ke/id/eprint/5225 |
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