一种改进的自适应和声搜索优化算法

doi:10.6040/j.issn.1671-9352.1.2015.076

山东大学学报（理学版） ›› 2016, Vol. 51 ›› Issue (1): 84-88.doi: 10.6040/j.issn.1671-9352.1.2015.076

一种改进的自适应和声搜索优化算法

葛彦强¹,汪向征²

1.安阳师范学院计算机与信息工程学院, 河南安阳 455002;2. 安阳师范学院传媒学院, 河南安阳 455002

收稿日期:2015-09-18 出版日期:2016-01-16 发布日期:2016-11-29
作者简介:葛彦强(1978— ),男,副教授,研究方向为模式识别、甲骨文信息处理.E-mail: geyanqiang@126.com
基金资助:
河南省国际科技合作计划项目(152102410041);河南省教育厅科学技术研究重点项目(14B520001、15A413007)

An improved self-adaptive harmony search optimization algorithm

GE Yan-qiang¹, WANG Xiang-zheng²

1. School of Computer &
Information Engineering, Anyang Normal University, Anyang 455002, Henan, China;
2. School of Communication, Anyang Normal University, Anyang 455002, Henan, China

Received:2015-09-18 Online:2016-01-16 Published:2016-11-29

摘要/Abstract

摘要： 在传统和声搜索优化算法的基础上,提出一种自适应双子和声搜索优化算法。通过建立主辅两个和声库正反双向进行迭代搜索,并对和声算法中的音调调整概率和音调微调带宽两个重要参数进行自适应调整,提高了算法的动态适应性以及局部搜索和全局搜索的协调能力。通过构造两组搜索方向各异、相互协同的主、辅和声,充分利用了搜索域内的隐含信息,扩展搜索范围,从而实现了全局最优。在试验中分别对3个复杂函数进行测试,结果表明该算法具有较好的全局搜索能力和收敛速度,在一定程度上提高了最优值的搜索能力,达到了预期效果。

关键词: 自适应, 参数调整, 和声搜索算法, 双子和声

Abstract: Based on the traditional harmony search optimization algorithm, a search optimization algorithm of self-adaptive gemini harmonies was proposed. By establishing two primary harmony and secondary harmony libraries, iterative searching along positive and negative directions, and adaptively adjusting two important parameters of pitch adjusting rate and band width of optimization algorithm, the algorithms dynamic adaptive ability and the coordination ability between local search and global search was improved. Two primary harmony and secondary harmony were constructed that were in different directions and cooperative, which made full use of the hidden information in the search domain, enhanced the searching area, and realized the global optimal. Three complex functions was tested in experiment, the results show that the algorithm had better global search ability and convergence rate compared with the original algorithm, the search ability of the optimal value was improved to some extent, which achieved the anticipated effects.

Key words: harmony search algorithm, self-adaption, gemini harmony, parameter adjustment

中图分类号:

TP391

葛彦强,汪向征. 一种改进的自适应和声搜索优化算法[J]. 山东大学学报（理学版）, 2016, 51(1): 84-88.

GE Yan-qiang, WANG Xiang-zheng. An improved self-adaptive harmony search optimization algorithm[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(1): 84-88.

参考文献

[1] ZONG Woo Geem, JOONG Hoon Kim, LOGANATHAN G V. A new heuristic optimization algorithm: harmony search[J]. Transactions of the Society for Modeling and Simulation International, 2001, 76(2):60-68.
[2] MAHDAVI M, FESANGHARY M, DAMANGIR E. An improved harmony search algorithm for solving optimization problems[J]. Applied Mathematics and Computation, 2007, 188(2):1567-1579.
[3] ZONG Woo Geem. Improved harmony search from ensemble of music players[J]. Knowledge-based Intelligent Information & Engineering Systems, 2006, 4251(10):86-93.
[4] MAHAMED G H Omran, MEHRDAD Mahdavi. Global-best harmony search[J]. Applied Mathematics and Computation, 2008, 198(2):643-656.
[5] PRITHWISH Chakraborty, GOURAB Ghosh Roy, SWAGATAM Das, et al. An improved harmony search algorithm with differential mutation operator[J]. Fundamenta Informaticae, 2009, 95(4):401-426.
[6] MAJID Jaberipour, ESMAILE Khorram.Two improved harmony search algorithms for solving engineering optimization problems[J]. Communications in Nonlinear Science & Numerical Simulation, 2010, 15(11):3316-3331.
[7] 何宗耀,郝伟.一种新颖的改进自适应和声算法[J].计算机应用与软件,2012,29(9):268-277. HE Zongyao, HAO Wei. A novel modified self-adaptive harmony search algorithm[J]. Computer Applications and Software, 2012, 29(9):268-277.
[8] 韩红燕,潘全科,梁静. 改进的和声搜索算法在函数优化中的应用[J].计算机工程,2010,36(13):245-247. HAN Hongyan, PAN Quanke, LIANG Jing. Application of improved harmony search algorithm in function optimization[J].Computer Engineering, 2010, 36(13):245-247.
[9] 拓守恒,邓方安.一种求解高维复杂优化问题的动态自适应和声搜索算法[J].计算机科学, 2012,39(9):240-246. TUO Shouheng, DENG Fangan. Dynamic self-adaptive harmony search algorithm for solving high-dimensional complex optimization problems[J]. Computer Science, 2012,39(9):240-246.
[10] 孙崇,孙子文.一种基于MCB的自适应和声搜索定位算法[J].传感器与微系统,2015,34(4):119-122. SUN Chong, SUN Ziwen. A self-adaptive harmony search localization algorithm based on MCB[J]. Transducer and Microsystem Technologies, 2015, 34(4):119-122.
[11] 拓守恒,雍龙泉,邓方安.动态调整策略改进的和声搜索算法[J].智能系统学报,2015,10(2):307-315. TUO Shouheng, YONG Longquan, DENG Fangan. Dynamic adjustment strategy for improving the harmony search algorithm[J]. CAAI Transactions on Intelligent Systems, 2015, 10(2):307-315.
[12] 刘立群,火久元,王联国,等.混合蛙跳细菌觅食的和声搜索算法及图像应用[J]. 计算机科学与探索,2015, 9(1):119-128. LIU Liqun, HUO Jiuyuan, WANG Lianguo, et al. Harmony search algorithm based on shuffled frog leaping and bacterial foraging and its application in image[J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(1):119-128.
[13] 李晓康.基于改进和声算法的有序样本聚类及其应用[J]. 陕西理工学院学报(自然科学版),2015,31(3):65-70. LI Xiaokang. Cluster of ordered sample and its application based on harmony search algorithm[J]. Journal of Shaanxi University of Technology(Natural Science Edition), 2015, 31(3):65-70.
[14] 张英杰,李亮,张英豪,等.一种基于双子群的改进粒子群优化算法[J].湖南大学学报(自然科学版),2011,38(1):84-88. ZHANG Yingjie, LI Liang, ZHANG Yinghao, et al. An improved particle swarm optimization algorithm based on two-subpopulation[J]. Journal of Hunan University(Natural Sciences), 2011,38(1):84-88.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

一种改进的自适应和声搜索优化算法

An improved self-adaptive harmony search optimization algorithm

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

多维度评价

本文评价

推荐阅读 0

[1]	晏燕,郝晓弘. 差分隐私密度自适应网格划分发布方法[J]. 山东大学学报（理学版）, 2018, 53(9): 12-22.
[2]	康海燕,黄渝轩,陈楚翘. 基于视频分析的地理信息隐私保护方法[J]. 山东大学学报（理学版）, 2018, 53(1): 19-29.
[3]	宋元章,李洪雨,陈媛,王俊杰. 基于分形与自适应数据融合的P2P botnet检测方法[J]. 山东大学学报（理学版）, 2017, 52(3): 74-81.
[4]	黄伟婷,赵红,祝峰. 代价敏感属性约简的自适应分治算法[J]. 山东大学学报（理学版）, 2016, 51(8): 98-104.
[5]	姚亮,洪宇,刘昊,刘乐,姚建民. 基于语义分布相似度的翻译模型领域自适应研究[J]. 山东大学学报（理学版）, 2016, 51(7): 43-50.
[6]	刘春梅, 钟柳强, 舒适, 肖映雄. 平面弹性问题的高次有限元离散系统的局部多重网格法[J]. 山东大学学报（理学版）, 2015, 50(08): 34-39.
[7]	张晶, 肖智斌, 容会, 崔毅. 改进型遗传算法在网络蜘蛛上的应用[J]. 山东大学学报（理学版）, 2015, 50(05): 1-6.
[8]	杨叶红,肖剑*,马珍珍. 一个新分数阶混沌系统的同步和控制[J]. 山东大学学报（理学版）, 2014, 49(2): 76-83.
[9]	吕小妮1,王艳彩2,高岳林2. BVaR风险度量下限制性卖空的单位风险收益最大投资组合模型[J]. J4, 2013, 48(05): 92-96.
[10]	周燕1,2,刘培玉1,2,赵静1,2,王乾龙1,2. 基于自适应惯性权重的混沌粒子群算法[J]. J4, 2012, 47(3): 27-32.
[11]	丁卫平1,2,3,王建东2,段卫华2,施佺1. 一种求解属性约简优化的协同粒子群算法[J]. J4, 2011, 46(5): 97-102.
[12]	王少波,高振明,李志勇,胡兰雨,王凤丽 . FMT系统中的动态比特分配算法性能分析[J]. J4, 2006, 41(6): 99-102 .