计算机科学 ›› 2015, Vol. 42 ›› Issue (11): 256-259.doi: 10.11896/j.issn.1002-137X.2015.11.052

• 人工智能 • 上一篇    下一篇

差分进化算法中参数自适应选择策略研究

汪慎文,张文生,丁立新,谢承旺,郭肇禄   

  1. 石家庄经济学院信息工程学院 石家庄050031;中国科学院自动化研究所 北京100190;武汉大学计算机学院软件工程国家重点实验室 武汉430072,中国科学院自动化研究所 北京100190,武汉大学计算机学院软件工程国家重点实验室 武汉430072,华东交通大学软件学院 南昌330013,江西理工大学理学院 赣州341000
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61165004,1),河北省青年拔尖人才支持计划(冀字[2013]),河北省自然科学青年基金项目(F2015403046),河北省科技支撑计划(13210331),河北省教育厅青年科学基金项目(QN20131053),石家庄经济学院博士科研启动基金项目(BQ201322),江西省教育厅青年科学基金项目(GJJ14456,GJJ14373),江西理工大学博士科研启动基金项目(JXXJBS13028)资助

Research on Parameter Self-selection Strategy of Differential Evolution

WANG Shen-wen, ZHANG Wen-sheng, DING Li-xin, XIE Cheng-wang and GUO Zhao-lu   

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

摘要: 参数选择本身是一个组合优化问题,尽管过去提出了很多方法,但是参数选择依然令人困惑,为此提出适用于差分进化算法的参数自适应选择策略。该策略在进化的过程中动态评估参数的性能,并根据其结果指导下一次迭代过程的参数选择。从参数库的建立、参数评分机制和参数配置机制3方面展开研究,对比实验结果表明,该方法效果良好。

关键词: 差分进化算法,参数自适应,参数选择

Abstract: The selection of the parameter itself is a combinatorial optimization problem.Although a considerable number of works have been conducted,it is known to be a puzzled task.In this paper,a DE algorithm was proposed that uses a new mechanism to parameter self-selection,which dynamically learns from their previous experiences and selects the best performing combinations of parameters for the next generation during the convergence process.We firstly designed the mechanism including three aspects:building of parameter database,score of parameter performance and selection of parameter combination,then we conducted the experiments on some benchmark functions to judge the performance.The results show that the DE with the new mechanism obtains promising performance.

Key words: Differential evolution,Parameter self-adaptation,Parameter selection

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