Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866929431151378432 |
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| author | Lin, Xi Zhang, Xiaoyuan Yang, Zhiyuan Liu, Fei Wang, Zhenkun Zhang, Qingfu |
| author_facet | Lin, Xi Zhang, Xiaoyuan Yang, Zhiyuan Liu, Fei Wang, Zhenkun Zhang, Qingfu |
| contents | Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In the past few decades, numerous methods have been proposed to find Pareto solutions that represent optimal trade-offs among the objectives for a given problem. However, these existing methods could have high computational complexity or may not have good theoretical properties for solving a general differentiable multi-objective optimization problem. In this work, by leveraging the smooth optimization technique, we propose a lightweight and efficient smooth Tchebycheff scalarization approach for gradient-based multi-objective optimization. It has good theoretical properties for finding all Pareto solutions with valid trade-off preferences, while enjoying significantly lower computational complexity compared to other methods. Experimental results on various real-world application problems fully demonstrate the effectiveness of our proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19078 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Smooth Tchebycheff Scalarization for Multi-Objective Optimization Lin, Xi Zhang, Xiaoyuan Yang, Zhiyuan Liu, Fei Wang, Zhenkun Zhang, Qingfu Machine Learning Artificial Intelligence Neural and Evolutionary Computing Optimization and Control Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In the past few decades, numerous methods have been proposed to find Pareto solutions that represent optimal trade-offs among the objectives for a given problem. However, these existing methods could have high computational complexity or may not have good theoretical properties for solving a general differentiable multi-objective optimization problem. In this work, by leveraging the smooth optimization technique, we propose a lightweight and efficient smooth Tchebycheff scalarization approach for gradient-based multi-objective optimization. It has good theoretical properties for finding all Pareto solutions with valid trade-off preferences, while enjoying significantly lower computational complexity compared to other methods. Experimental results on various real-world application problems fully demonstrate the effectiveness of our proposed method. |
| title | Smooth Tchebycheff Scalarization for Multi-Objective Optimization |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing Optimization and Control |
| url | https://arxiv.org/abs/2402.19078 |