Evolutionary Methods for Solving Multi-Objective Optimization Problem
| dc.contributor.advisor | Mitiku, Semu | |
| dc.contributor.author | Woldegebriel, Girum | |
| dc.date.accessioned | 2018-07-16T08:01:29Z | |
| dc.date.accessioned | 2023-11-04T12:32:21Z | |
| dc.date.available | 2018-07-16T08:01:29Z | |
| dc.date.available | 2023-11-04T12:32:21Z | |
| dc.date.issued | 2012-06 | |
| dc.description.abstract | Evolutionary methods are characterized as a set of solution based algorithms to solve multi-objective optimization problems. Evolutionary algorithms have a potential of finding multiple Pareto optimal solution in a single simulation run. In this report we have considered non-dominated sorting genetic algorithm to solve multi objective optimization problem. We have suggested non-dominated sorting genetic algorithm–II for minimization of the objectives. Non-dominated sorting genetic algorithm–II is fast elitist search algorithm which is based on non-domination rank. Non- domination rank provides chance to the population to be chosen to become parent of the next generation. Selection is based on crowded comparison operator to pick population to variation operator | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/8680 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Evolutionary methods are characterized | en_US |
| dc.subject | As a set of solution based algorithms | en_US |
| dc.title | Evolutionary Methods for Solving Multi-Objective Optimization Problem | en_US |
| dc.type | Thesis | en_US |