Liu Xiangyong, Yang Zan, Liu Jiansheng, Xiong Junxing, Huang Jihui, Huang Shuiyuan, Fu Xuedong
School of Advanced Manufacturing, Nanchang University, Nanchang, 330031, China.
Jiangxi Tellhow Sci-Tech Co., Ltd, Nanchang, 330031, China.
Sci Rep. 2025 Jan 11;15(1):1728. doi: 10.1038/s41598-025-85233-6.
This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed collaboratively at each generation. As the complexity of optimization problems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions. For global surrogate-assisted collaborative evolution phase, the global candidate set is generated through classification collaborative mutation operations to alleviate the pre-screening pressure of the surrogate model. For local surrogate-assisted phase, a distributed central region local exploration is designed to achieve intensively search for promising distributed local areas which are located by affinity propagation clustering and mathematical modeling. More importantly, a three-layer adaptive selection strategy where the feasibility, diversity and convergence are balanced effectively is designed to identify promising solutions in global and local candidate sets. Therefore, the SGDLCO efficiently balances global and local search during the whole optimization process. Experimental studies on five classical test suites demonstrate that the SGDLCO provides excellent performance in solving expensive constrained optimization problems.
本文提出了一种用于昂贵约束优化问题的代理辅助全局与分布式局部协同优化（SGDLCO）算法，该算法在每一代协同执行两个代理优化阶段。随着实际应用中优化问题的复杂性和求解成本的增加，如何利用有限的计算资源高效地解决昂贵约束优化问题已成为一个重要的研究领域。传统优化算法往往难以平衡全局搜索和局部搜索的效率，特别是在处理高维和复杂约束条件时。对于全局代理辅助协同进化阶段，通过分类协同变异操作生成全局候选集，以减轻代理模型的预筛选压力。对于局部代理辅助阶段，设计了一种分布式中心区域局部探索方法，以对通过亲和传播聚类和数学建模定位的有前景的分布式局部区域进行密集搜索。更重要的是，设计了一种三层自适应选择策略，有效平衡可行性、多样性和收敛性，以在全局和局部候选集中识别有前景的解。因此，SGDLCO在整个优化过程中有效地平衡了全局搜索和局部搜索。对五个经典测试套件的实验研究表明，SGDLCO在解决昂贵约束优化问题方面具有优异的性能。
