一种求解约束优化问题的自适应差分进化算法

doi:10.3969/j.issn.0372-2112.2016.10.036

电子学报 ›› 2016, Vol. 44 ›› Issue (10): 2535-2542.DOI: 10.3969/j.issn.0372-2112.2016.10.036

一种求解约束优化问题的自适应差分进化算法

閤大海¹, 李元香¹, 龚文引², 何国良¹

1. 武汉大学软件工程国家重点实验室, 武汉大学计算机学院, 湖北武汉 430072;
2. 中国地质大学(武汉)计算机学院, 湖北武汉 430074

收稿日期:2015-11-20 修回日期:2016-01-07 出版日期:2016-10-25
- 作者简介:
- 閤大海,男,1981年生于湖北随州.现为武汉大学计算机学院博士生.主要研究方向为演化计算,约束优化.E-mail:xdh628@163.com;李元香,男,1962年出生于湖北监利,武汉大学计算机学院软件工程国家重点实验室教授,博士生导师,主要研究方向为演化计算的理论与应用研究.E-mail:yxli@whu.edu.cn;龚文引,男,1979年出生于湖南永顺,博士,中国地质大学(武汉)计算机学院副教授,主要研究方向为演化计算及应用.E-mail:wygong@cug.edu.cn
- 基金资助:
- 国家重大仪器专项 (No.2011YQ170065.4）; 国家自然科学基金 (No.61573324）

An Adaptive Differential Evolution Algorithm for Constrained Optimization Problems

XIA Da-hai¹, LI Yuan-xiang¹, GONG Wen-yin², HE Guo-liang¹

1. College of Computer Science, Wuhan University. Wuhan, Hubei 430072, China;
2. School of Computer Science, China University of Geosciences. Wuhan, Hubei 430074, China

Received:2015-11-20 Revised:2016-01-07 Online:2016-10-25 Published:2016-10-25
- Supported by:
- National Major Instrument Project (No.2011YQ170065.4); National Natural Science Foundation of China (No.61573324)

摘要/Abstract

摘要：

自适应算子选择方式已被用于差分进化算法求解全局优化问题及多目标优化问题，然而在求解约束优化时难于为自适应算子选择方式找到一种方式来恰当分配信用.为此，本文提出了一种基于混合种群的自适应适应值方式来对约束优化问题中变异策略进行信用分配并采用概率匹配方法自适应选择差分变异策略，同时对算法变异缩放因子与交叉率进行自适应设置提高算法的成功率.实验结果表明算法在求解约束优化问题相比于CODEA/OED，ATMES，εBBO-dm，COMDE以及εDE算法有较高的收敛精度及收敛速度，同时验证了自适应方式的有效性.该算法可用于预报、质量控制、会计过程等科学和工程应用领域.

关键词: 约束优化, 差分进化算法, 自适应, 信用分配, 概率匹配

Abstract:

The adaptive operator selection method is used to solve the global optimization problem and multi-objective optimization problem of differential evolution algorithm.However,it is difficult to find a way to properly allocate credit for the adaptive operator selection in solving the constrained optimization problem.In order to realize the adaptive strategy selection in differential evolution,we present a combined population based adaptive fitness method to achieve the credit assignment of mutate strategies for constrained optimization problems and use probability matching method to select the mutate strategy adaptively.And we also set the mutation scaling factor and the crossover rate adaptively to improve the success rate of the algorithm.Experimental results show that the algorithm has higher accuracy and convergence speed comparing to CODEA/OED,ATMES,εBBO-dm,COMDE and εDE.We also test and verify the effectiveness of the adaptive method.The algorithm can be used in forecasting,quality control,accounting process,and other scientific and engineering applications.

Key words: constrained optimization, differential evolution algorithm, adaptation, credit assignment, probability matching

中图分类号:

TP18

閤大海, 李元香, 龚文引, 等. 一种求解约束优化问题的自适应差分进化算法[J]. 电子学报, 2016, 44(10): 2535-2542.

XIA Da-hai, LI Yuan-xiang, GONG Wen-yin, et al. An Adaptive Differential Evolution Algorithm for Constrained Optimization Problems[J]. Acta Electronica Sinica, 2016, 44(10): 2535-2542.

参考文献

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一种求解约束优化问题的自适应差分进化算法

An Adaptive Differential Evolution Algorithm for Constrained Optimization Problems

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