Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization

• URL: http://arxiv.org/abs/2605.10260v1
• Date: Mon, 11 May 2026 09:25:35 GMT
• Title: Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
• Abstract summary: We propose a bi-level MetaBBO framework for expensive constrained multi-objective optimization problems.<n>A meta-policy provides search guidance to the low-level Surrogate-Assisted Evolutionary Algorithm (SAEA)<n>We show that MetaSG-SAEA outperforms state-of-the-art baselines across diverse benchmarks.
• Score: 2.3308436514095874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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