[1]陶新民,徐鹏,刘福荣,等.组合分布估计和差分进化的多目标优化算法[J].智能系统学报,2013,8(01):39-45.[doi:10.3969/j.issn.1673-4785.201208035]
　TAO Xinmin,XU Peng,LIU Furong,et al.Multi objective optimization algorithm composed of estimation of distribution and differential evolution[J].CAAI Transactions on Intelligent Systems,2013,8(01):39-45.[doi:10.3969/j.issn.1673-4785.201208035]
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组合分布估计和差分进化的多目标优化算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年01期
页码:
39-45
栏目:
出版日期:
2013-03-25

文章信息/Info

Title:
Multi objective optimization algorithm composed of estimation of distribution and differential evolution
文章编号:
1673-4785(2013)01-0039-07
作者:
陶新民 1; 徐鹏 1; 刘福荣 2; 张冬雪 1
1.哈尔滨工程大学 信息与通信工程学院，黑龙江 哈尔滨 150001；
 2.黑龙江省电力有限公司 科信处，黑龙江 哈尔滨 150090
Author(s):
TAO Xinmin 1;  XU Peng 1;  LIU Furong 2;  ZHANG Dongxue 1
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Science and Information Department, Heilongjiang Electric Power Company Limited, Harbin 150090, China
关键词:
多目标优化; 分布估计算法; 差分进化算法
Keywords:
multi objective optimization;  estimation of distribution algorithm;  differential evolution algorithm
分类号:
TP18
DOI:
10.3969/j.issn.1673-4785.201208035
文献标志码:
A
摘要:
为了提高多目标优化算法的收敛能力及求解精度，提出了一种组合分布估计和差分进化的多目标优化算法.该方法用分布估计算法和差分进化算法共同生成种群中的粒子，利用选择因子来控制每个粒子的产生方式，并且根据迭代次数的增加来改变2种算法的使用比例，搜索初期利用分布估计算法进行快速定位，然后用差分进化算法进行精确搜索.并对差分进化算法的变异因子进行了改进，定义了一个可变的变异因子，来控制不同搜索时期中差分进化算法的变异范围.用4个测试函数对算法进行了仿真测试，并同NSGA Ⅱ和RM MEDA进行了比较.实验结果表明，该算法具有良好的收敛性和分布性，并且效果稳定.
Abstract:
In order to improve the ability of convergence and accuracy of a multi objective optimization algorithm, a multi objective optimization algorithm composed of estimation of distribution and differential evolution has been proposed. Both estimation of distribution algorithm and differential evolution algorithm will be used to generate particles of population. The generation method of each particle has been decided by using a selective factor, and proportion of the use of two algorithms according to the frequency of iterations. Utilizing an estimation of distribution algorithm to quickly locate in the initial search, and then differential evolution algorithm was used for accurately conducting searches. The variation factor of differential evolution algorithm was improved, and a variable variation factor also was defined and used to control the range of variation of differential evolution algorithm in different search periods. Four test functions were used to evaluate the performance of the proposed algorithm, and next the proposed algorithm was compared with nondominated sorting genetic algorithm II (NSGA II) and regularity model based multiobjective estimation of distribution algorithm (RM MEDA). The experimental results show that the proposed algorithm displayed a good convergence, diversity performance, and the stable effects.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2012-08-27.
网络出版日期：2013-01-25 .
基金项目：国家自然科学基金资助项目(61074076)；中国博士后科学基金资助项目(20090450119)；中国博士点新教师基金资助项目(20092304120017).
通信作者：徐鹏.
E-mail: newadress@126.com.
作者简介：
陶新民，男，1973年生，副教授，硕士生导师，博士，主要研究方向为自然计算、数据简约、故障诊断.发表学术论文20余篇，其中被EI检索8篇.
徐鹏，男，1987年生，硕士研究生，主要研究方向为自然计算、信号处理.
刘福荣，女，1970年生，副教授，博士，主要研究方向为故障诊断、群智能计算、人工智能.
更新日期/Last Update: 2013-04-12