An ensemble approach to multi-objective evolutionary algorithm
Multi-objective optimization refers to the procedure of obtaining a set of feasible solution for multiple objective functions. Based on the no free lunch (NFL) theorem, an optimization technique would never exceed all other optimization techniques on every type of optimization problem. Ensemble appr...
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| Format: | Thesis |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/78420 |
| _version_ | 1826118987461689344 |
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| author | Pratama, Januar Ananta Dinar |
| author2 | Ponnuthurai N. Suganthan |
| author_facet | Ponnuthurai N. Suganthan Pratama, Januar Ananta Dinar |
| author_sort | Pratama, Januar Ananta Dinar |
| collection | NTU |
| description | Multi-objective optimization refers to the procedure of obtaining a set of feasible solution for multiple objective functions. Based on the no free lunch (NFL) theorem, an optimization technique would never exceed all other optimization techniques on every type of optimization problem. Ensemble approach is one method to improve the performance of the multi-objective algorithm. This method is combining two or more multi-objective algorithms to get the benefit of each individual algorithm.
An ensemble of multi-objective optimization with three multi-objective optimization algorithms (MOEA/D, NSGA-III, SMODE) has been implemented on the multi-objective benchmark test function (a set of many and multi-objective bound constrained benchmark problems). The ensemble method has the best performance in comparison to its former individual algorithm. The results of the simulation show the ensemble has better Pareto-optimal front based on the convergence, diversity and quantitative performance. |
| first_indexed | 2024-10-01T04:52:38Z |
| format | Thesis |
| id | ntu-10356/78420 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:52:38Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/784202023-07-04T16:20:43Z An ensemble approach to multi-objective evolutionary algorithm Pratama, Januar Ananta Dinar Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Multi-objective optimization refers to the procedure of obtaining a set of feasible solution for multiple objective functions. Based on the no free lunch (NFL) theorem, an optimization technique would never exceed all other optimization techniques on every type of optimization problem. Ensemble approach is one method to improve the performance of the multi-objective algorithm. This method is combining two or more multi-objective algorithms to get the benefit of each individual algorithm. An ensemble of multi-objective optimization with three multi-objective optimization algorithms (MOEA/D, NSGA-III, SMODE) has been implemented on the multi-objective benchmark test function (a set of many and multi-objective bound constrained benchmark problems). The ensemble method has the best performance in comparison to its former individual algorithm. The results of the simulation show the ensemble has better Pareto-optimal front based on the convergence, diversity and quantitative performance. Master of Science (Computer Control and Automation) 2019-06-19T14:16:10Z 2019-06-19T14:16:10Z 2019 Thesis http://hdl.handle.net/10356/78420 en 46 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Pratama, Januar Ananta Dinar An ensemble approach to multi-objective evolutionary algorithm |
| title | An ensemble approach to multi-objective evolutionary algorithm |
| title_full | An ensemble approach to multi-objective evolutionary algorithm |
| title_fullStr | An ensemble approach to multi-objective evolutionary algorithm |
| title_full_unstemmed | An ensemble approach to multi-objective evolutionary algorithm |
| title_short | An ensemble approach to multi-objective evolutionary algorithm |
| title_sort | ensemble approach to multi objective evolutionary algorithm |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/78420 |
| work_keys_str_mv | AT pratamajanuaranantadinar anensembleapproachtomultiobjectiveevolutionaryalgorithm AT pratamajanuaranantadinar ensembleapproachtomultiobjectiveevolutionaryalgorithm |