昂贵多模态优化问题的代理辅助进化算法综述OA

Expensive multimodal optimization problems(EMMOPs)arise in engineering design frequently and are often characterized as multimodal properties and with extremely high evaluation costs.The progress and key tech-niques of surrogate-assisted evolutionary algorithms(SAEAs)for such problems were systematically reviewed in the study.Firstly,typical surrogate models,including polynomial regression model and Gaussian process,were intro-duced,with emphasis on their characteristics and applicability in sample fitting,nonlinear representation,and un-certainty quantification.Then,the general framework of SAEAs was summarized,and the main design ideas of ex-isting algorithms were outlined in terms of single-surrogate and multi-surrogate structures,global-local collaborative search,and infill sampling strategies.Subsequently,according to the different characteristics of EMMOPs,typical EMMOPs,including single-objective,multi-objective,constrained,and high-dimensional problems,were system-atically categorized and reviewed,with particular attention to advances in mode identification,solution diversity preservation,and computational budget allocation.Furthermore,experimental comparisons of multiple mainstream SAEAs were conducted on ten benchmark test problems,and the performance differences among various algorithms were analyzed in terms of metrics such as global optimum solution and effective valley ratio.Meanwhile,engineer-ing case studies,including ship structure optimization and synchronous machine design in ultra-high-voltage direct current transmission systems,were incorporated to illustrate the application potential of surrogate-assisted evolution-ary algorithms in complex engineering optimization.Finally,the key challenges faced by current research were summarized,and future development directions were discussed from the perspectives of adaptive surrogate model management,parallel execution and scheduling,as well as inter-modal information sharing and transfer mechanisms.