Naïve differential evolution algorithm

doi:10.11772/j.issn.1001-9081.2015.05.1333

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1333-1335.DOI: 10.11772/j.issn.1001-9081.2015.05.1333

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Naïve differential evolution algorithm

WANG Shenwen^1,2, ZHANG Wensheng², QIN Jin³, XIE Chengwang⁴, GUO Zhaolu⁵

1. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang Hebei 050031, China;
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
3. School of Computer, Guizhou University, Guiyang Guizhou 550025, China;
4. School of Software, East China Jiaotong University, Nanchang Jiangxi 330013, China;
5. School of Science, Jiangxi University of Science and Technology, Ganzhou Guangdong 34100, China

Received:2014-12-29 Revised:2015-02-06 Online:2015-05-10 Published:2015-05-14

朴素差分进化算法

汪慎文^1,2, 张文生², 秦进³, 谢承旺⁴, 郭肇禄⁵

1. 石家庄经济学院信息工程学院, 石家庄 050031;
2. 中国科学院自动化研究所, 北京 100190;
3. 贵州大学计算机学院, 贵阳 550025;
4. 华东交通大学软件学院, 南昌 330013;
5. 江西理工大学理学院, 江西赣州 341000

通讯作者: 汪慎文
作者简介:汪慎文(1979-),男,湖北红安人,副教授,博士,CCF会员,主要研究方向:智能计算、机器学习; 张文生(1966-),男,河南郑州人,研究员,博士生导师,博士,主要研究方向:模式识别、机器学习; 秦进(1978-),男,贵州黔西人,副教授,博士,主要研究方向:智能计算;谢承旺(1974-),男,湖北武汉人,副教授,博士,主要研究方向:智能计算; 郭肇禄(1984-),男,江西南康人,讲师,博士,主要研究方向:智能计算、并行计算.
基金资助:
国家自然科学基金资助项目(61165004,61402481);河北省青年拔尖人才支持计划项目(冀字[2013]);河北省自然科学基金资助项目(F2015403046);河北省科技支撑计划项目(13210331);河北省教育厅青年科学基金资助项目(QN20131053);石家庄经济学院博士科研启动基金资助项目(BQ201322);江西省教育厅青年科学基金资助项目(GJJ14456,GJJ14373).

Abstract

Abstract:

In order to solve singleness of mutation study, a naïve mutation strategy was proposed to approach the best individual and depart the worst one. So, a scale factor self-adaptation mechanism was used and the parameter was set to a small value when the dimension value of three random individuals is very close to each other, otherwise, set it to a large value. The results showed that the Differential Evolution (DE) with the new mechanism exhibits a robust convergence behavior measured by average number of fitness evaluations, successful running rate and acceleration rate.

Key words: Differential Evolution (DE), naï, ve mutation operator, scale factor, integrated evolution

摘要：

针对变异算子学习方式的单一性,提出一种朴素变异算子,其基本思想是向优秀的个体靠近,同时远离较差个体,其实现方式是设计一种缩放因子调整策略,如果三个随机个体在某维上比较接近,则缩放因子变小,反之变大.在实验过程中通过平均适应度评价次数、成功运行次数和加速比等指标表明,基于朴素变异算子的差分进化算法能有效提高算法的收敛速度和健壮性.

关键词: 差分进化, 朴素变异算子, 缩放因子, 集成进化

CLC Number:

TP301

WANG Shenwen, ZHANG Wensheng, QIN Jin, XIE Chengwang, GUO Zhaolu. Naïve differential evolution algorithm[J]. Journal of Computer Applications, 2015, 35(5): 1333-1335.

汪慎文, 张文生, 秦进, 谢承旺, 郭肇禄. 朴素差分进化算法[J]. 计算机应用, 2015, 35(5): 1333-1335.

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Naïve differential evolution algorithm

朴素差分进化算法

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