标题:Best Guided Backtracking Search Algorithm for Numerical Optimization Problems
作者:Zhao, Wenting; Wang, Lijin; Wang, Bingqing; Yin, Yilong
通讯作者:Yin, Yilong
作者机构:[Zhao, Wenting; Wang, Lijin; Wang, Bingqing; Yin, Yilong] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.; [Wang, Lijin] Fujia 更多
会议名称:9th International Conference on Knowledge Science, Engineering, and Management (KSEM)
会议日期:OCT 05-07, 2016
来源:KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2016
出版年:2016
卷:9983
页码:414-425
DOI:10.1007/978-3-319-47650-6_33
关键词:Backtracking search algorithm; Best guided; Historical information;; Numerical optimization problems
摘要:Backtracking search algorithm is a promising stochastic search technique by using its historical information to guide the population evolution. Using historical population information improves the exploration capability, but slows the convergence, especially on the later stage of iteration. In this paper, a best guided backtracking search algorithm, termed as BGBSA, is proposed to enhance the convergence performance. BGBSA employs the historical information on the beginning stage of iteration, while using the best individual obtained so far on the later stage of iteration. Experiments are carried on the 28 benchmark functions to test BGBSA, and the results show the improvement in efficiency and effectiveness of BGBSA.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:1
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992648254&doi=10.1007%2f978-3-319-47650-6_33&partnerID=40&md5=43381cc7dff69b0b869b88d251e53c26
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