二阶段循环优化差分演化算法

doi:10.3969/j.issn.0372-2112.2013.12.021

电子学报 ›› 2013, Vol. 41 ›› Issue (12): 2456-2461.DOI: 10.3969/j.issn.0372-2112.2013.12.021

二阶段循环优化差分演化算法

周雅兰¹, 王甲海², 林琛²

1. 广东商学院信息学院, 广东广州 510320;
2. 中山大学计算机科学系, 广东广州 510006

收稿日期:2012-09-27 修回日期:2013-01-18 出版日期:2013-12-25
- 作者简介:
- 周雅兰 女，1979年3月出生于湖南省常德市.现为广东商学院信息学院副教授.主要研究方向为人工智能与数据挖掘. E-mail：zhouylan@163.com 王甲海 男，1977年6月出生于江西省赣州市.现为中山大学计算机系副教授，从事人工智能及其应用的研究工作. E-mail：wangjiah@mail.sysu.edu.cn林琛男，1986年出生于广东汕头.研究生.主要研究方向为智能优化算法及其应用.
- 基金资助:
- 国家自然科学基金 (No.60905038，No.60805026，No.61070076）; 广州市珠江科技新星; 广东商学院科研创新团队建设计划

Framework of Recurring Two-Stage Differential Evolution

ZHOU Ya-lan¹, WANG Jia-hai², LIN Chen²

1. Information Science School, Guangdong University of Business Studies, Guangzhou, Guangdong 510320, China;
2. Department of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong 510006, China

Received:2012-09-27 Revised:2013-01-18 Online:2013-12-25 Published:2013-12-25

摘要/Abstract

摘要： 差分演化算法具有结构简单容易实现，收敛速度快和鲁棒性强等优点，但是存在早熟和进化停滞的现象.提出的二阶段循环优化差分演化算法框架能够很好地保持算法局部开采能力和全局勘探能力的平衡.在差分演化的变异操作中，以马氏距离矩阵为依据分别在目标向量的近邻或者远邻中选择父辈个体参与变异，这样分别形成偏重局部开采或者偏重全局勘探的搜索阶段，此二阶段循环迭代，使得局部开采和全局勘探能力得到震荡平衡.在CEC2005标准函数集上的测试结果显示了提出算法框架的有效性.

关键词: 差分演化, 二阶段循环优化, 局部开采, 全局勘探, 连续优化

Abstract: The advantages of differential evolution(DE) are its simple structure,easiness of implement,fast convergence and robustness.However,DE often suffers from premature convergence and stagnation problems.A framework of the recurring two-stage DE is proposed to balance global exploration and local exploitation.The proposed framework is based on repeated and alternated execution of two different stages,namely,the local exploitation and global exploration stages.The parent individuals for the mutation operation at each stage are selected from neighbors or strangers of the target vector,respectively,based on the Mahalanobis distance matrix.The simulation results on the CEC2005 real-parameter optimization benchmark functions show that the proposed framework can make DE more efficient.

Key words: differential evolution, recurring two-stage optimization, local exploitation, global exploration, continuous optimization

中图分类号:

TP18

周雅兰, 王甲海, 林琛. 二阶段循环优化差分演化算法[J]. 电子学报, 2013, 41(12): 2456-2461.

ZHOU Ya-lan, WANG Jia-hai, LIN Chen. Framework of Recurring Two-Stage Differential Evolution[J]. Acta Electronica Sinica, 2013, 41(12): 2456-2461.

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二阶段循环优化差分演化算法

Framework of Recurring Two-Stage Differential Evolution

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