Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918292897136640 |
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| author | Lin, Xi Liu, Yilu Zhang, Xiaoyuan Liu, Fei Wang, Zhenkun Zhang, Qingfu |
| author_facet | Lin, Xi Liu, Yilu Zhang, Xiaoyuan Liu, Fei Wang, Zhenkun Zhang, Qingfu |
| contents | Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods often focus on finding a set of Pareto solutions with different optimal trade-offs among the objectives. However, the required number of solutions to well approximate the whole Pareto optimal set could be exponentially large with respect to the number of objectives, which makes these methods unsuitable for handling many optimization objectives. In this work, instead of finding a dense set of Pareto solutions, we propose a novel Tchebycheff set scalarization method to find a few representative solutions (e.g., 5) to cover a large number of objectives (e.g., $>100$) in a collaborative and complementary manner. In this way, each objective can be well addressed by at least one solution in the small solution set. In addition, we further develop a smooth Tchebycheff set scalarization approach for efficient optimization with good theoretical guarantees. Experimental studies on different problems with many optimization objectives demonstrate the effectiveness of our proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19650 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization Lin, Xi Liu, Yilu Zhang, Xiaoyuan Liu, Fei Wang, Zhenkun Zhang, Qingfu Machine Learning Artificial Intelligence Neural and Evolutionary Computing Optimization and Control Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods often focus on finding a set of Pareto solutions with different optimal trade-offs among the objectives. However, the required number of solutions to well approximate the whole Pareto optimal set could be exponentially large with respect to the number of objectives, which makes these methods unsuitable for handling many optimization objectives. In this work, instead of finding a dense set of Pareto solutions, we propose a novel Tchebycheff set scalarization method to find a few representative solutions (e.g., 5) to cover a large number of objectives (e.g., $>100$) in a collaborative and complementary manner. In this way, each objective can be well addressed by at least one solution in the small solution set. In addition, we further develop a smooth Tchebycheff set scalarization approach for efficient optimization with good theoretical guarantees. Experimental studies on different problems with many optimization objectives demonstrate the effectiveness of our proposed method. |
| title | Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing Optimization and Control |
| url | https://arxiv.org/abs/2405.19650 |