文献类型：期刊文献

英文题名：Enhancing Landscape Approximation With Ensemble-Based Surrogate Model for Expensive Constrained Multiobjective Optimization

作者：Li, Yingwei[1,2]; Feng, Xiang[1,2]; Yu, Huiqun[1,2]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, 200237, China; [2] Shanghai Engineering Research Center of Smart Energy, China

年份：2025

外文期刊名：IEEE Transactions on Evolutionary Computation

收录：EI(收录号：20251718309480)

语种：英文

外文关键词：Benchmarking - Constraint handling - Multiobjective optimization - Optimization algorithms - Population statistics

摘要：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. ? 2025 IEEE.