一种基于反向学习的约束差分进化算法

doi:10.3969/j.issn.0372-2112.2016.02.026

电子学报 ›› 2016, Vol. 44 ›› Issue (2): 426-436.DOI: 10.3969/j.issn.0372-2112.2016.02.026

一种基于反向学习的约束差分进化算法

魏文红¹, 周建龙², 陶铭¹, 袁华强¹

1. 东莞理工学院计算机学院, 广东东莞 523808;
2. 西安交通大学城市学院计算机系, 陕西西安 710018

收稿日期:2015-04-10 修回日期:2015-07-08 出版日期:2016-02-25
- 通讯作者:
- 袁华强
- 作者简介:
- 魏文红 男,1977年9月生于江西南昌,东莞理工学院副教授,博士,主要研究方向:高性能计算、进化算法、多目标优化处理.E-mail:weiwh@dgut.edu.cn;周建龙 男,1974年6月出生于甘肃临洮,西安交通大学城市学院特聘教授,博士,研究兴趣包括:人机交互、体视化、增强现实、认知计算以及机器学习.E-mail:zhou_jianlong@hotmail.com;陶铭男,1986年6月生于安徽马鞍山,东莞理工学院副研究员,博士,主要研究方向:移动IP技术.E-mail:taom@dgut.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61103037,No.61300198); 广东省自然科学基金 (No.S2013010011858); 广东省高校科技创新项目 (No.2013KJCX0178); 陕西省工业科技攻关项目 (No.2015GY012); 陕西省自然科学基础研究计划项目 (No.2015JM6331); 西安交通大学城市学院科研项目 (No.2015KZ01,2015KZ02)

Constrained Differential Evolution Using Opposition-Based Learning

WEI Wen-hong¹, ZHOU Jian-long², TAO Ming¹, YUAN Hua-qiang¹

1. School of Computer, Dongguan University of Technology, Dongguan, Guangdong 523808, China;
2. Department of Computer, Xi'an Jiaotong University City College, Xi'an, Shaanxi 710018

Received:2015-04-10 Revised:2015-07-08 Online:2016-02-25 Published:2016-02-25
- Supported by:
- National Natural Science Foundation of China (No.61103037, No.61300198); National Natural Science Foundation of Guangdong Province, China (No.S2013010011858); Science and Technology Innovation Program of Colleges and Universities in Guangdong Province (No.2013KJCX0178); Industrial Science and Technology Research and Development Project of Shaanxi Province (No.2015GY012); Supported by the Natural Science Basis Research Plan in Shaanxi Province of China (No.2015JM6331); Science Research Program of City College of Xi 'an Jiaotong University (No.2015KZ01, 2015KZ02)

摘要/Abstract

摘要：

差分进化算法是一种结构简单、易用且鲁棒性强的全局搜索启发式优化算法,它可以结合约束处理技术来解决约束优化问题.机器学习在进化算法中,经常可以引导种群的进化,而且被广泛地应用于无约束的差分进化算法中,但对于约束差分进化算法却很少有应用.针对这一情况,提出了一种基于反向学习的约束差分进化算法框架.该算法框架采用基于反向学习的机器学习方法,提高约束差分进化算法的多样性和加速全局收敛速度.最后把该算法框架植入了两个著名的约束差分进化算法:(μ+λ)-CDE和ECHT,并采用CEC 2010的18个Benchmark函数进行了实验评估,实验结果表明:与(μ+λ)-CDE和ECHT相比,植入后的算法具有更强的全局搜索能力、更快的收敛速度和更高的收敛精度.

关键词: 反向学习, 差分进化, 约束优化, 收敛性

Abstract:

Differential evolution is a global heuristic algorithm, which is simple, easy-to-use and robust in practice.Combining with the constraint-handling techniques, it can solve constrained optimization problems.Machine learning often guides population to evolve in the evolution computation, and is widely applied to unconstrained differential evolution algorithm.However, machine learning is rarely applied to constrained differential evolution algorithm, so this paper proposed a constrained differential evolution algorithm framework using opposition-based learning.The algorithm can improve the diversity and convergence of differential evolution.At last, the proposed algorithm framework is applied to two popular constrained differential evolution variants, that is (μ+λ) -CDE and ECHT-DE.And 18 benchmark functions presented in CEC 2010 are chosen as the test suite, experimental results show that comparing with (μ+λ) -CDE and ECHT-DE, our algorithms are able to improve global search ability, convergence speed and accuracy in the majority of test cases.

Key words: opposition-based learning, differential evolution, constrained optimization, convergence

中图分类号:

TP38

魏文红, 周建龙, 陶铭, 等. 一种基于反向学习的约束差分进化算法[J]. 电子学报, 2016, 44(2): 426-436.

WEI Wen-hong, ZHOU Jian-long, TAO Ming, et al. Constrained Differential Evolution Using Opposition-Based Learning[J]. Acta Electronica Sinica, 2016, 44(2): 426-436.

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一种基于反向学习的约束差分进化算法

Constrained Differential Evolution Using Opposition-Based Learning

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