An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA
Decomposition-based evolutionary multiobjective algorithms (MOEAs) divide a multiobjective problem into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good overall performance especially in maintaining population diversity. However, they enco...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8870356 |
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| _version_ | 1832555150856683520 |
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| author | Wenbo Qiu Jianghan Zhu Huangchao Yu Mingfeng Fan Lisu Huo |
| author_facet | Wenbo Qiu Jianghan Zhu Huangchao Yu Mingfeng Fan Lisu Huo |
| author_sort | Wenbo Qiu |
| collection | DOAJ |
| description | Decomposition-based evolutionary multiobjective algorithms (MOEAs) divide a multiobjective problem into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good overall performance especially in maintaining population diversity. However, they encounter huge difficulties in addressing problems with irregular Pareto fronts (PFs) since many reference vectors do not work during the searching process. To cope with this problem, this paper aims to improve an existing decomposition-based algorithm called reference vector-guided evolutionary algorithm (RVEA) by designing an adaptive reference vector adjustment strategy. By adding the strategy, the predefined reference vectors will be adjusted according to the distribution of promising solutions with good overall performance and the subspaces in which the PF lies may be further divided to contribute more to the searching process. Besides, the selection pressure with respect to convergence performance posed by RVEA is mainly from the length of normalized objective vectors and the metric is poor in evaluating the convergence performance of a solution with the increase of objective size. Motivated by that, an improved angle-penalized distance (APD) method is developed to better distinguish solutions with sound convergence performance in each subspace. To investigate the performance of the proposed algorithm, extensive experiments are conducted to compare it with 5 state-of-the-art decomposition-based algorithms on 3-, 5-, 8-, and 10-objective MaF1–MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance. |
| format | Article |
| id | doaj-art-703999e7051944b0964f69800388dc27 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-703999e7051944b0964f69800388dc272025-02-03T05:49:29ZengWileyComplexity1099-05262021-01-01202110.1155/2021/8870356An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEAWenbo Qiu0Jianghan Zhu1Huangchao Yu2Mingfeng Fan3Lisu Huo4College of Systems EngineeringCollege of Systems EngineeringInstitute of Unmanned SystemSchool of Traffic and Transportation EngineeringCollege of Systems EngineeringDecomposition-based evolutionary multiobjective algorithms (MOEAs) divide a multiobjective problem into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good overall performance especially in maintaining population diversity. However, they encounter huge difficulties in addressing problems with irregular Pareto fronts (PFs) since many reference vectors do not work during the searching process. To cope with this problem, this paper aims to improve an existing decomposition-based algorithm called reference vector-guided evolutionary algorithm (RVEA) by designing an adaptive reference vector adjustment strategy. By adding the strategy, the predefined reference vectors will be adjusted according to the distribution of promising solutions with good overall performance and the subspaces in which the PF lies may be further divided to contribute more to the searching process. Besides, the selection pressure with respect to convergence performance posed by RVEA is mainly from the length of normalized objective vectors and the metric is poor in evaluating the convergence performance of a solution with the increase of objective size. Motivated by that, an improved angle-penalized distance (APD) method is developed to better distinguish solutions with sound convergence performance in each subspace. To investigate the performance of the proposed algorithm, extensive experiments are conducted to compare it with 5 state-of-the-art decomposition-based algorithms on 3-, 5-, 8-, and 10-objective MaF1–MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance.http://dx.doi.org/10.1155/2021/8870356 |
| spellingShingle | Wenbo Qiu Jianghan Zhu Huangchao Yu Mingfeng Fan Lisu Huo An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA Complexity |
| title | An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA |
| title_full | An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA |
| title_fullStr | An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA |
| title_full_unstemmed | An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA |
| title_short | An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA |
| title_sort | adaptive reference vector adjustment strategy and improved angle penalized value method for rvea |
| url | http://dx.doi.org/10.1155/2021/8870356 |
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