计算机科学 ›› 2015, Vol. 42 ›› Issue (6): 247-250.doi: 10.11896/j.issn.1002-137X.2015.06.052

• 人工智能 • 上一篇    下一篇

多变异策略的自适应差分演化算法

周雅兰,徐 志   

  1. 广东财经大学信息学院 广州510320,中山大学信息科学与技术学院 广州510006
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受广州市珠江科技新星专项(2012J2200085),广东省教育厅高校优秀青年创新人才培育项目(2012LYM_0066),广东商学院科研创新团队建设计划资助

Self-adaptive Differential Evolution with Multi-mutation Strategies

ZHOU Ya-lan and XU Zhi   

  • Online:2018-11-14 Published:2018-11-14

摘要: 差分演化(Differential Evolution,DE)算法的性能依赖于变异策略的选择和控制参数的设置。不同问题对DE的变异策略和参数的设置各不相同。为了提高DE的性能,提出一种多变异策略的自适应差分演化算法,建立由多种变异策略组成的策略池,两个主要参数自适应策略控制。为了验证所提算法的性能,在测试数据集CEC2013上进行了实验,并将其与使用6种不同变异策略的原始DE和4种改进DE进行比较。实验结果表明,提出的算法是一种有效的DE变种,其性能优于其它DE。

关键词: 差分演化算法,多变异策略,参数自适应

Abstract: The performance of differential evolution(DE) algorithm often depends heavily on the mutation strategy and control parameters.A novel self-adaptive differential evolution with multi-mutation strategies called SMSDE was proposed.SMSDE designs a strategy pool consisting of many kinds of mutation strategy and applies self-adaptive strategies to two main parameters.In order to verify the performance of SMSDE,SMSDE was compared with 6 original DEs and 4 advanced DEs on CEC2013 benchmark functions.The experimental results show that SMSDE is superior to original DEs,and is competitive with the current advanced DE variants.

Key words: Differential evolution algorithm,Multi-mutation strategies,Parameter self-adaptation

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