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baroshi木虫 (正式写手)
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[求助]
帮忙查一篇文章的SCI号，谢谢。
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A co-evolutionary muti-objective optimization algorithm based on direction vectors [ 发自手机版 http://muchong.com/3g ] |
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sunshan4379
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【答案】应助回帖
★ ★ ★ ★ ★
感谢参与，应助指数 +1
baroshi: 金币+5, ★★★★★最佳答案 2014-09-21 19:07:06
oven1986: LS-EPI+1, 感谢应助！ 2014-09-22 19:30:13
感谢参与，应助指数 +1
baroshi: 金币+5, ★★★★★最佳答案 2014-09-21 19:07:06
oven1986: LS-EPI+1, 感谢应助！ 2014-09-22 19:30:13
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A co-evolutionary multi-objective optimization algorithm based on direction vectors 作者:Jiao, LC (Jiao, L. C.)[ 1 ] ; Wang, HD (Wang, Handing)[ 1 ] ; Shang, RH (Shang, R. H.)[ 1 ] ; Liu, F (Liu, F.)[ 1 ] INFORMATION SCIENCES 卷: 228 页: 90-112 DOI: 10.1016/j.ins.2012.12.013 出版年: APR 10 2013 查看期刊信息 摘要 Most real world multi-objective problems (MOPs) have a complicated solution space. Facing such problems, a direction vectors based co-evolutionary multi-objective optimization algorithm (DVCMOA) that introduces the decomposition idea from MOEA/D to co-evolutionary algorithms is proposed in this paper. It is novel in the sense that DVCMOA applies the concept of direction vectors to co-evolutionary algorithms. DVCMOA first divides the entire population into several subpopulations on the basis of the initial direction vectors in the objective space. Then, it solves MOPs through the co-evolutionary interaction among the subpopulations in which individuals are classified according to their direction vectors. Finally, it explores the less developed regions to maintain the relatively uniform distribution of the solution space. In this way, DVCMOA has advantages in convergence, diversity and uniform distribution of the non-dominated solution set, which are explained through comparison with other state-of-the-art multi-objective optimization evolutionary algorithms (MOEAs) in this paper. DVCMOA is shown to be effective on 6 multi-objective 0-1 knapsack problems. Crown Copyright (C) 2012 Published by Elsevier Inc. All rights reserved. 关键词 作者关键词:Multi-objective optimization; Co-evolutionary; Direction vector; Pareto set; MOEA/D KeyWords Plus:GENETIC ALGORITHM; COMPETITIVE COEVOLUTION; PARETO FRONT; MOEA/D 作者信息 通讯作者地址: Wang, HD (通讯作者) Minist Educ China, Sch Elect Engn, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China. 地址: [ 1 ] Minist Educ China, Sch Elect Engn, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China 电子邮件地址:jlcxidian@163.com; wanghanding@163.com; rhshang@iiip.xidian.edu.cn; fliu@iiip.xidian.edu.cn 基金资助致谢 基金资助机构 授权号 National Natural Science Foundation of China 61001202 61072139 60872135 60803098 61003199 Provincial Natural Science Foundation of Shaanxi of China 2009JQ8015 2011JQ8010 2010JQ8023 China Post-Doctoral Science Foundation 201104658 20090451369 20080431228 200801426 National Research Foundation for the Doctoral Program of Higher Education of China 200807010003 20100203120008 20090203120016 Fundamental Research Funds for the Central Universities K50510020001 K50510020011 Fund for Foreign Scholars in University Research and Teaching Programs B07048 National Science and Technology Ministry of China 9140A07011810DZ0107 9140A07021010DZ0131 Key Scientific and Technological Innovation Special Projects of Shaanxi "13115" 2008ZDKG-37 查看基金资助信息 出版商 ELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 类别 / 分类 研究方向:Computer Science Web of Science 类别:Computer Science, Information Systems 文献信息 文献类型:Article 语种:English 入藏号: WOS:000315245800007 ISSN: 0020-0255 电子 ISSN: 1872-6291 期刊信息 Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: 094EM Web of Science 核心合集中的 "引用的参考文献": 58 Web of Science 核心合集中的 "被引频次": 3 |
2楼2014-09-21 18:01:12
sunshan4379
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3楼2014-09-21 18:01:34
