东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2013, Vol. 34 ›› Issue (9): 1217-1221.DOI: -

• 信息与控制 •    下一篇

混沌反向学习和声搜索算法

欧阳海滨1，高立群1，郭丽2，孔祥勇1   

  1. (1.东北大学信息科学与工程学院，辽宁沈阳110819；2.天津医科大学医学影像学院，天津300203)
  • 收稿日期:2013-03-10 修回日期:2013-03-10 出版日期:2013-09-15 发布日期:2013-04-22
  • 通讯作者: 欧阳海滨
  • 作者简介:欧阳海滨(1987-)，男，湖南郴州人，东北大学博士研究生；高立群(1949-)，男，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(81000639).

Chaos OppositionBased Learning Harmony Search Algorithm

OUYANG Haibin1， GAO Liqun1， GUO Li2， KONG Xiangyong1   

  1. 1. School of Information Science ＆ Engineering， Northeastern University， Shenyang 110819， China; 2. School of Medical Imaging， Tianjin Medical University， Tianjin 300203， China.
  • Received:2013-03-10 Revised:2013-03-10 Online:2013-09-15 Published:2013-04-22
  • Contact: OUYANG Haibin
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摘要： 为改善和声搜索算法易陷入局部最优的不足，提出了一种混沌反向学习和声搜索(COLHS)算法.基于聚集和发散思想，对算法陷入局部最优和停滞状态进行初步预判断，并根据预判断的结果融合混沌扰动策略和反向学习，利用了logistic混沌序列的遍历性和反向学习的空间可扩展性.此外，利用和声记忆库的历史信息定义更新因子和进化因子，自适应地调整参数基音调整概率(PAR)和基音调整步长(BW)，平衡算法的聚集和发散.数值结果表明，COLHS算法优于HS算法及最近文献报道的8种改进的HS算法.

关键词: 和声搜索算法, 混沌扰动策略, 反向学习, 局部最优, 历史信息

Abstract: Harmony search (HS) algorithm is easily trapped into local optimal. To improve this shortcoming， chaos oppositionbased learning harmony search (COLHS) algorithm was proposed. Based on the thought of aggregation and divergence， preliminary judgments whether this algorithm was trapped into local optimal or backwater status were given， then according to the judge result， disturbance strategy was integrated with oppositionbased learning technology. The ergodicity of logistic chaos sequence and the space extensibility of oppositionbased learning were used. Besides， to balance aggregation and divergence， the history information of harmony memory was used to define the updating factor and the evolution factor， which were applied to dynamically adjust the pitch adjustment rate (PAR) and the bandwidth (BW). Numerical results demonstrated that the proposed algorithm is better than HS and the other eight kinds of improved HS algorithms that reported in recent literatures.

Key words: harmony search algorithm, chaos disturbance strategy, oppositionbased learning, local optimal, history information

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