摘要
针对黑箱约束优化中可行采样点难以获取、优化效率低、收敛速度慢、难以有效平衡全局和局部搜索行为等缺点,提出一种序列Kriging模型的黑箱约束全局优化方法。在没有初始可行采样点的情况下,所提出的方法能够快速有效地探索出富有前景的可行采样点,并在满足所有约束条件下通过高效稳定且可靠的加点采样准则获取全局最优可行解。基准测试函数和燃料电池汽车能量控制策略的仿真实例验证了所提出方法的有效性和实用性。
Aiming at the shortcomings that it is difficult to obtain feasible points,low optimization efficiency,slow convergence speed,and difficult to effectively balance the global and local search behaviors,a black-box constrained global optimization method of sequential Kriging(BCGO-SK)is proposed.In the absence of initial feasible sampling points,the proposed method can quickly and efficiently explore promising feasible points,and obtain the global optimal feasible solution by satisfying all constraints under the efficient,stable and reliable sampling point.The test results on benchmark functions and a simulation of fuel cell vehicle energy control strategies verify the effectiveness and practicability of the proposed method.
作者
师路欢
付虹
李耀辉
SHI Luhuan;FU Hong;LI Yaohui(School of Electrical Engineering&Mechanic Engineering,Xuchang University,Xuchang 461000,China;College of Electronics and Electrical Engineering,Changchun University of Technology,Changchun 130012,China)
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2022年第3期481-487,共7页
Journal of Xinyang Normal University(Natural Science Edition)
基金
国家自然科学基金项目(51775472)
河南省高校科技创新人才项目(21HASTIT027)
河南省优秀青年基金项目(202300410346)。
关键词
约束全局优化
代理模型
序列Kriging
加点采样准则
constrained global optimization
surrogate models
sequential Kriging
infill sampling criteria
