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基于反馈的精英教学优化算法

于坤杰 王昕 王振雷

于坤杰, 王昕, 王振雷. 基于反馈的精英教学优化算法. 自动化学报, 2014, 40(9): 1976-1983. doi: 10.3724/SP.J.1004.2014.01976
引用本文: 于坤杰, 王昕, 王振雷. 基于反馈的精英教学优化算法. 自动化学报, 2014, 40(9): 1976-1983. doi: 10.3724/SP.J.1004.2014.01976
YU Kun-Jie, WANG Xin, WANG Zhen-Lei. Elitist Teaching-learning-based Optimization Algorithm Based on Feedback. ACTA AUTOMATICA SINICA, 2014, 40(9): 1976-1983. doi: 10.3724/SP.J.1004.2014.01976
Citation: YU Kun-Jie, WANG Xin, WANG Zhen-Lei. Elitist Teaching-learning-based Optimization Algorithm Based on Feedback. ACTA AUTOMATICA SINICA, 2014, 40(9): 1976-1983. doi: 10.3724/SP.J.1004.2014.01976

基于反馈的精英教学优化算法

doi: 10.3724/SP.J.1004.2014.01976
基金项目: 

国家重点基础研究发展计划(973计划)(2012CB720500),国家自然科学基金(61333010,21276078,21206037),中央高校基本科研业务费专项资金(863计划)(2013AA0400701),上海市科技攻关(12dz1125100),十二五国家科技支撑计划(2012BAF05B00),上海市重点学科建设项目(B504),上海市自然科学基金(14ZR1421800),流程工业综合自动化国家重点实验室开放课题基金资助项目(PAL-N201404)资助

详细信息
    作者简介:

    于坤杰 华东理工大学信息科学与工程学院博士研究生.主要研究方向为智能优化算法.E-mail:yukunjie1990@gmail.com

    通讯作者:

    王振雷 华东理工大学教授.主要研究方向为智能控制,复杂系统的建模及特征分析,故障诊断和智能优化算法.本文通信作者.E-mail:wangzhenl@ecust.edu.cn

Elitist Teaching-learning-based Optimization Algorithm Based on Feedback

Funds: 

Supported by National Basic Research Program of China (973 Program)(2012CB720500), National Natural Science Foundation of China (61333010, 21276078, 21206037), The Central University Basic Scientific Research Business Expenses Special Funds (863 Program)(2013AA0400701), Shanghai Science and Technology Research Projects (12dz1125100), National Science and Technology Support Project during the 12th Five-Year Plan Period (2012BAF05B00), Shanghai Leading Academic Discipline Project (B504), Shanghai Natural Science Foundation (14ZR1421800), the State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201404)

  • 摘要: 精英教学优化算法(Elitist teaching-learning-based optimization,ETLBO)是一种基于实际班级教学过程的新型优化算法. 本文针对ETLBO算法寻优精度低、稳定性差的问题,提出了反馈精英教学优化算法(Feedback ETLBO). 在ETLBO算法的基础上,通过在学生阶段之后加入反馈阶段,增加了学生的学习方式,保持学生的多样性特性,提高算法的全局搜索能力. 同时,反馈阶段是选举成绩较差的学生与教师交流,使成绩较差的学生快速向教师靠拢,使算法进行局部精细搜索,提高算法的寻优精度. 对6个无约束及5个约束标准函数的测试结果表明,FETLBO算法与其他算法相比在寻优精度和稳定性上更具优势. 最后将FETLBO算法应用于拉压弹簧优化设计问题及0-1背包问题,取得了满意结果.
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出版历程
  • 收稿日期:  2013-07-08
  • 修回日期:  2014-02-26
  • 刊出日期:  2014-09-20

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