Expensive Constrained Optimization via Adaptive Switching Between Surrogate-Assisted Constrained and Unconstrained Search
针对昂贵约束优化问题，提出一种代理辅助差分进化算法，在约束搜索与无约束搜索间自适应切换，并动态调整选择比例，实验表明优于五种现有方法。
Expensive constrained optimization problems are prevalent in many engineering domains, where evaluating objective and constraints requires costly simulations or physical experiments. As powerful optimization methods in artificial intelligence, surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated effectiveness in solving expensive constrained optimization problems. However, most existing SAEAs focus solely on surrogate-assisted constrained search, overlooking the potential benefits offered by surrogate-assisted unconstrained search. Therefore, a surrogate-assisted differential evolution with adaptive switching between constrained and unconstrained search is proposed for expensive constrained optimization. The proposed method aims to integrate both surrogate-assisted constrained and unconstrained search modes within a unified framework, enabling adaptive switching between the two search modes during the optimization process. Moreover, a selection ratio update mechanism is proposed to dynamically adjust the selection ratio between the two search modes in accordance with their contributions to algorithm performance. Experimental results on test suites and a real-world engineering application demonstrate that the proposed method outperforms five state-of-the-art SAEAs in solving expensive constrained optimization problems.