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Enhancing Landscape Approximation With Ensemble-Based Surrogate Model for Expensive Constrained Multiobjective Optimization
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AI
DOI：10.1109/TEVC.2025.3563383.png)
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Abstract
En 中文
Expensive constrained multiobjective optimization problems (ECMOPs) are prevalent in real-world scientific research and industrial applications. However, the complexity of feasible regions and the limitation on the number of available function evaluations often prevent most algorithms from achieving satisfactory results. To address these challenges, this article proposes an ensemble-based surrogate framework. Specifically, a global model and multiple local models are constructed as ensemble members to approximate each constraint function, aiming to improve the accuracy of landscape approximation for ECMOPs with complex feasible regions. Additionally, a novel vector-based constrained dominance principle is suggested to maintain the balance between objectives and constraints. By leveraging reference vectors, potential scenarios of the population during the evolutionary process are identified, and the customized selection strategy is devised for each scenario. These two techniques are integrated into a two-stage optimization framework, resulting in a surrogate-assisted evolutionary algorithm for solving ECMOPs. Through extensive experimental investigations, the proposed algorithm demonstrates significant superiority over seven other state-of-the-art peer algorithms on both benchmark test problems and real-world applications.
Keywords:
Constraint handling technique (CHT)
ensemble-based surrogate model
expensive constrained multiobjective optimization
surrogate-assisted evolutionary algorithm (SAEA)
Journal
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12
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1.8K
Citations:
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