标题:Sequential quadratic programming enhanced backtracking search algorithm
作者:Zhao, Wenting; Wang, Lijin; Yin, Yilong; Wang, Bingqing; Tang, Yuchun
作者机构:[Zhao, Wenting; Wang, Lijin; Yin, Yilong; Wang, Bingqing] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China.; [Wang, Li 更多
通讯作者:Yin, Yilong
通讯作者地址:[Yin, YL]Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China.
来源:FRONTIERS OF COMPUTER SCIENCE
出版年:2018
卷:12
期:2
页码:316-330
DOI:10.1007/s11704-016-5556-9
关键词:numerical optimization; backtracking search algorithm; sequential; quadratic programming; local search
摘要:In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm (BSA) and sequential quadratic programming (SQP). BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031500782&doi=10.1007%2fs11704-016-5556-9&partnerID=40&md5=fe7cd811cc3da219f019776a51d19bb1
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