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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
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
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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 |
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