A genetic algorithm for unconstrained multi-objective optimization
In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional multi-objective optimization method whose aim is to find a single Pareto so...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Elsevier
2015
|
| Online Access: | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/4334 |
| _version_ | 1848744486828507136 |
|---|---|
| author | Long, Q. Wu, Changzhi Huang, T. Wang, Xiangyu |
| author_facet | Long, Q. Wu, Changzhi Huang, T. Wang, Xiangyu |
| author_sort | Long, Q. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional multi-objective optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-objective optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-objective benchmarks and compare its results with other MOGASs; the result show that the proposed method is robust and efficient. |
| first_indexed | 2025-11-14T06:02:14Z |
| format | Journal Article |
| id | curtin-20.500.11937-4334 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:02:14Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-43342023-02-02T03:24:11Z A genetic algorithm for unconstrained multi-objective optimization Long, Q. Wu, Changzhi Huang, T. Wang, Xiangyu In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional multi-objective optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-objective optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-objective benchmarks and compare its results with other MOGASs; the result show that the proposed method is robust and efficient. 2015 Journal Article http://hdl.handle.net/20.500.11937/4334 10.1016/j.swevo.2015.01.002 http://purl.org/au-research/grants/arc/LP140100873 Elsevier restricted |
| spellingShingle | Long, Q. Wu, Changzhi Huang, T. Wang, Xiangyu A genetic algorithm for unconstrained multi-objective optimization |
| title | A genetic algorithm for unconstrained multi-objective optimization |
| title_full | A genetic algorithm for unconstrained multi-objective optimization |
| title_fullStr | A genetic algorithm for unconstrained multi-objective optimization |
| title_full_unstemmed | A genetic algorithm for unconstrained multi-objective optimization |
| title_short | A genetic algorithm for unconstrained multi-objective optimization |
| title_sort | genetic algorithm for unconstrained multi-objective optimization |
| url | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/4334 |