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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.05721 |
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| _version_ | 1866912044743131136 |
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| author | Wang, Hao Rodriguez-Fernandez, Angel E. Uribe, Lourdes Deutz, André Cortés-Piña, Oziel Schütze, Oliver |
| author_facet | Wang, Hao Rodriguez-Fernandez, Angel E. Uribe, Lourdes Deutz, André Cortés-Piña, Oziel Schütze, Oliver |
| contents | A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algorithms have proven to be very efficient in finding good Pareto front approximations, they may need quite a few resources or may even fail to obtain optimal or nearly approximations. Hereby, optimality is implicitly defined by the chosen performance indicator. In this work, we propose a set-based Newton method for Hausdorff approximations of the Pareto front to be used within multi-objective evolutionary algorithms. To this end, we first generalize the previously proposed Newton step for the performance indicator for the treatment of constrained problems for general reference sets. To approximate the target Pareto front, we propose a particular strategy for generating the reference set that utilizes the data gathered by the evolutionary algorithm during its run. Finally, we show the benefit of the Newton method as a post-processing step on several benchmark test functions and different base evolutionary algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms Wang, Hao Rodriguez-Fernandez, Angel E. Uribe, Lourdes Deutz, André Cortés-Piña, Oziel Schütze, Oliver Neural and Evolutionary Computing A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algorithms have proven to be very efficient in finding good Pareto front approximations, they may need quite a few resources or may even fail to obtain optimal or nearly approximations. Hereby, optimality is implicitly defined by the chosen performance indicator. In this work, we propose a set-based Newton method for Hausdorff approximations of the Pareto front to be used within multi-objective evolutionary algorithms. To this end, we first generalize the previously proposed Newton step for the performance indicator for the treatment of constrained problems for general reference sets. To approximate the target Pareto front, we propose a particular strategy for generating the reference set that utilizes the data gathered by the evolutionary algorithm during its run. Finally, we show the benefit of the Newton method as a post-processing step on several benchmark test functions and different base evolutionary algorithms. |
| title | A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2405.05721 |