Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917479986495488 |
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| author | Du, Yukun Yu, Haiyue Jiang, Jiang Tang, Shuaiwen Xie, Xiaotong Liu, Haobo Hu, Chongshuang Chang, Shengkun |
| author_facet | Du, Yukun Yu, Haiyue Jiang, Jiang Tang, Shuaiwen Xie, Xiaotong Liu, Haobo Hu, Chongshuang Chang, Shengkun |
| contents | Existing Meta-Black-Box Optimization (MetaBBO) methods focus on how to search when controlling optimizers, but largely overlook where to search. We propose MetaSG-SAEA, a bi-level MetaBBO framework for expensive constrained multi-objective optimization problems (ECMOPs), in which a meta-policy provides search guidance to the low-level Surrogate-Assisted Evolutionary Algorithm (SAEA). To achieve this, we introduce Max-Min Constraint-Calibrated Inequality (MM-CCI), a compact, problem-agnostic region abstraction that maps heterogeneous constraint evaluations to an ordered scalar level; we further provide a theoretical analysis of its fundamental properties. Building on this region abstraction, we adopt diffusion-based population initialization to translate the meta-policy's region-level guidance into solution-level priors for the SAEA. To make MetaSG-SAEA scalable, we construct an attention-based state representation across varying problem dimensions, population sizes, and numbers of objectives and constraints. Experimental results demonstrate that MetaSG-SAEA outperforms state-of-the-art baselines across diverse benchmarks and exhibits the ability to generalize across problem distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10260 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization Du, Yukun Yu, Haiyue Jiang, Jiang Tang, Shuaiwen Xie, Xiaotong Liu, Haobo Hu, Chongshuang Chang, Shengkun Neural and Evolutionary Computing Existing Meta-Black-Box Optimization (MetaBBO) methods focus on how to search when controlling optimizers, but largely overlook where to search. We propose MetaSG-SAEA, a bi-level MetaBBO framework for expensive constrained multi-objective optimization problems (ECMOPs), in which a meta-policy provides search guidance to the low-level Surrogate-Assisted Evolutionary Algorithm (SAEA). To achieve this, we introduce Max-Min Constraint-Calibrated Inequality (MM-CCI), a compact, problem-agnostic region abstraction that maps heterogeneous constraint evaluations to an ordered scalar level; we further provide a theoretical analysis of its fundamental properties. Building on this region abstraction, we adopt diffusion-based population initialization to translate the meta-policy's region-level guidance into solution-level priors for the SAEA. To make MetaSG-SAEA scalable, we construct an attention-based state representation across varying problem dimensions, population sizes, and numbers of objectives and constraints. Experimental results demonstrate that MetaSG-SAEA outperforms state-of-the-art baselines across diverse benchmarks and exhibits the ability to generalize across problem distributions. |
| title | Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2605.10260 |