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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 novel selection evolutionary strategy for constrained optimization [ 发自手机版 http://muchong.com/3g ] |
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sunshan4379
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3楼2014-09-21 18:02:45
sunshan4379
版主 (文坛精英)
- LS-EPI: 131
- 应助: 218 (大学生)
- 贵宾: 7.08
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- 红花: 419
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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 18:30:01
oven1986: LS-EPI+1, 感谢应助！ 2014-09-22 19:30:23
感谢参与，应助指数 +1
baroshi: 金币+5, ★★★★★最佳答案 2014-09-21 18:30:01
oven1986: LS-EPI+1, 感谢应助！ 2014-09-22 19:30:23
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A novel selection evolutionary strategy for constrained optimization 作者:Jiao, LC (Jiao, LiCheng)[ 1 ] ; Li, L (Li, Lin)[ 1 ] ; Shang, RH (Shang, RongHua)[ 1 ] ; Liu, F (Liu, Fang)[ 1 ] ; Stolkin, R (Stolkin, Rustam)[ 2 ] INFORMATION SCIENCES 卷: 239 页: 122-141 DOI: 10.1016/j.ins.2013.03.002 出版年: AUG 1 2013 查看期刊信息 摘要 The existence of infeasible solutions makes it very difficult to handle constrained optimization problems (COPS) in a way that ensures efficient, optimal and constraint-satisfying convergence. Although further optimization from feasible solutions will typically lead in a direction that generates further feasible solutions, certain infeasible solutions can also provide useful information about the optimal direction of improvement for the objective function. How well an algorithm makes use of these two solutions determines its performance on COPs. This paper proposes a novel selection evolutionary strategy (NSES) for constrained optimization. A self-adaptive selection method is introduced to exploit both informative infeasible and feasible solutions from a perspective of combining feasibility with multi-objective problem (MOP) techniques. Since the global optimal solution of a COP is a feasible non-dominated solution, both non-dominated solutions with low constraint violation and feasible ones with low objective values are beneficial to an evolution process. Thus, the exploration and exploitation of both of these two kinds of solutions are preferred during the selection procedure. Several theorems and properties are given to prove the above assertion. Furthermore, the performance of our method is evaluated using 22 well-known benchmark functions. Experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of the speed of finding feasible solutions and the stability of converging to global optimal solutions. In particular, when dealing with problems that have zero feasibility ratios and more than one active constraint, our method provides feasible solutions within fewer fitness evaluations (FES) and converges to the optimal solutions more reliably than other popular methods from the literature. (C) 2013 Elsevier Inc. All rights reserved. 关键词 作者关键词:Non-dominated solution; Evolutionary algorithm; Constrained optimization; Multi-objective optimization; Constraint handling KeyWords Plus:ADAPTIVE PENALTY STRATEGY; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; COVERING ARRAYS; SEARCH; FORMULATION; OBJECTIVES; RANKING; SCHEME 作者信息 通讯作者地址: Li, L (通讯作者) 显示增强组织信息的名称 Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian, Peoples R China. 地址: 显示增强组织信息的名称 [ 1 ] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian, Peoples R China 显示增强组织信息的名称 [ 2 ] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England 电子邮件地址:xdlinli86@163.com 基金资助致谢 基金资助机构 授权号 National Natural Science Foundation of China 61001202 61003199 China Post-Doctoral Science Foundation 201104658 20090451369 National Research Foundation for the Doctoral Program of Higher Education of China 20100203120008 200807010003 0090203120016 Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) B07048 Program for Cheung Kong Scholars and Innovative Research Team in University IRT1170 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:000319538500009 ISSN: 0020-0255 期刊信息 Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: 152NN Web of Science 核心合集中的 "引用的参考文献": 49 Web of Science 核心合集中的 "被引频次": 1 |
2楼2014-09-21 18:02:32
