Enhancing Landscape Approximation With Ensemble-Based Surrogate Model for Expensive Constrained Multiobjective Optimization
针对昂贵约束多目标优化问题，提出集成代理框架，用全局和局部模型组合近似约束函数，并设计基于向量的约束支配原则，在基准测试和实际应用中优于七种先进算法。
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.