A Surrogate-based Optimization Algorithm with Local Search

被引：0
作者：
Yu, Mingyuan [1 ]
Qu, Shaocheng [2 ]
Wu, Zhou [1 ]
机构：
[1] Chongqing Univ, Sch Automat, Chongqing, Peoples R China
[2] Cent China Normal Univ, Dept Elect & Informat Engn, Wuhan, Peoples R China
来源：
2018 IEEE SYMPOSIUM ON PRODUCT COMPLIANCE ENGINEERING - ASIA 2018 (IEEE ISPCE-CN 2018) | 2018年
基金：
中国国家自然科学基金;
关键词：
Effective global optimization; Surrogate model; Local search; Antenna design; NEIGHBORHOOD FIELD OPTIMIZATION; EFFICIENT GLOBAL OPTIMIZATION; SAMPLING CRITERIA; DESIGN;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In 5th generation (5G) network, the optimized design of high-performance antenna has been an important and complicated issue. In this paper, to further improve the performance of surrogate-based global optimization algorithms (EGO) for 'black-book' problem, a neighborhood field search strategy is incorporated into the original EGO algorithm to make up for the insufficient local search. The resulted mimetic algorithm is called NFSEGO. In the evolution process, a valid local search is performed near certain promising candidate solutions. The new solution obtained by the local search will replace the current better candidate solution. To validate the proposed algorithm, five well-known benchmark functions, and one antenna optimization design engineering problem are studied. The presented results show that NFSEGO is able to excavate more excellent solution than original algorithm in terms of accuracy.
引用
收藏
页码：1 / 7
页数：7
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