Dealing with Structure Constraints in Evolutionary Pareto Set Learning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929330238521344 |
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| author | Lin, Xi Zhang, Xiaoyuan Yang, Zhiyuan Zhang, Qingfu |
| author_facet | Lin, Xi Zhang, Xiaoyuan Yang, Zhiyuan Zhang, Qingfu |
| contents | In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this work, we make the first attempt to incorporate the structure constraints into the whole solution set by a single Pareto set model, which can be efficiently learned by a simple evolutionary stochastic optimization method. With our proposed method, the decision-makers can flexibly trade off the Pareto optimality with preferred structures among all solutions, which is not supported by previous MOEAs. A set of experiments on benchmark test suites and real-world application problems fully demonstrates the efficiency of our proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_20426 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dealing with Structure Constraints in Evolutionary Pareto Set Learning Lin, Xi Zhang, Xiaoyuan Yang, Zhiyuan Zhang, Qingfu Neural and Evolutionary Computing In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this work, we make the first attempt to incorporate the structure constraints into the whole solution set by a single Pareto set model, which can be efficiently learned by a simple evolutionary stochastic optimization method. With our proposed method, the decision-makers can flexibly trade off the Pareto optimality with preferred structures among all solutions, which is not supported by previous MOEAs. A set of experiments on benchmark test suites and real-world application problems fully demonstrates the efficiency of our proposed method. |
| title | Dealing with Structure Constraints in Evolutionary Pareto Set Learning |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2310.20426 |