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A modified objective function method with feasible-guiding strategy to solve constrained multi-objective optimization problems

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baroshi: 金币+5, ★★★★★最佳答案 2014-09-21 19:06:34
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A modified objective function method with feasible-guiding strategy to solve constrained multi-objective optimization problems

作者:Jiao, LC (Jiao, Licheng)[ 1 ] ; Luo, JJ (Luo, Juanjuan)[ 1 ] ; Shang, RH (Shang, Ronghua)[ 1 ] ; Liu, F (Liu, Fang)[ 1 ]

APPLIED SOFT COMPUTING

卷: 14

页: 363-380

子辑: C

DOI: 10.1016/j.asoc.2013.10.008

出版年: JAN 2014

查看期刊信息
摘要

For constrained multi-objective optimization problems (CMOPs), how to preserve infeasible individuals and make use of them is a problem to be solved. In this case, a modified objective function method with feasible-guiding strategy on the basis of NSGA-II is proposed to handle CMOPs in this paper. The main idea of proposed algorithm is to modify the objective function values of an individual with its constraint violation values and true objective function values, of which a feasibility ratio fed back from current population is used to keep the balance, and then the feasible-guiding strategy is adopted to make use of preserved infeasible individuals. In this way, non-dominated solutions, obtained from proposed algorithm, show superiority on convergence and diversity of distribution, which can be confirmed by the comparison experiment results with other two CMOEAs on commonly used constrained test problems. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.
关键词

作者关键词:Constrained multi-objective optimization; Constraint handling; Modified objective function method; Feasible-guiding strategy

KeyWords Plus:EVOLUTIONARY ALGORITHMS; PARAMETER OPTIMIZATION; GENETIC ALGORITHMS
作者信息

通讯作者地址: Luo, JJ (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 710071, 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 710071, Peoples R China

电子邮件地址:ljjinxd@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        
200807010003
20100203120008
20090203120016
Fund for Foreign Scholars in University Research and Teaching Programs        
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 BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
类别 / 分类

研究方向:Computer Science

Web of Science 类别:Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications
文献信息

文献类型:Article

语种:English

入藏号: WOS:000327529200005

ISSN: 1568-4946

电子 ISSN: 1872-9681
期刊信息

    目录: Current Contents Connect®

    Impact Factor (影响因子): Journal Citation Reports®

其他信息

IDS 号: 259LU

Web of Science 核心合集中的 "引用的参考文献": 40

Web of Science 核心合集中的 "被引频次": 0
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muse

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入藏号: WOS:000327529200005
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