[1]周欢,李煜.具有动态惯性权重的布谷鸟搜索算法[J].智能系统学报编辑部,2015,10(04):645-651.[doi:10.3969/j.issn.1673-4785.201409042]
 ZHOU Huan,LI Yu.Cuckoo search algorithm with dynamic inertia weight[J].CAAI Transactions on Intelligent Systems,2015,10(04):645-651.[doi:10.3969/j.issn.1673-4785.201409042]
点击复制

具有动态惯性权重的布谷鸟搜索算法(/HTML)
分享到:

《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年04期
页码:
645-651
栏目:
出版日期:
2015-08-25

文章信息/Info

Title:
Cuckoo search algorithm with dynamic inertia weight
作者:
周欢1 李煜2
1. 河南大学 商学院, 河南 开封 475004;
2. 河南大学 管理科学与工程研究所, 河南 开封 475004
Author(s):
ZHOU Huan1 LI Yu2
1. School of Business Administration, He’nan University, Kaifeng 475004, China;
2. Research Institute of Management Science and Engineering, He’nan University, Kaifeng 475004, China
关键词:
布谷鸟搜索算法函数优化莱维飞行动态惯性权重种群规模收敛性复杂度参数选取
Keywords:
cuckoo search algorithmfunction optimizationLévy flightdynamic inertia weightpopulation sizeconvergencecomplexityparameter selection
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-4785.201409042
文献标志码:
A
摘要:
为提高布谷鸟搜索算法的搜索能力和寻优精度,提出一种具有动态惯性权重的布谷鸟搜索算法。该算法引入动态惯性权重改进鸟窝位置的更新方式,依据动态惯性权重值保留上代鸟窝的最优位置并进行下一代位置更新,从而有效平衡种群探索能力和开发能力之间的关系。并利用特征方程对改进算法进行了收敛性分析。仿真实验结果表明,与基本布谷鸟搜索算法、粒子群算法和蚁群算法相比,改进后的布谷鸟搜索算法能显著减少迭代次数和运行时间,有效提高算法的收敛速度和收敛精度。
Abstract:
In order to improve the search ability and optimization accuracy of cuckoo search algorithm, the cuckoo search with dynamic inertia weight is proposed. By utilizing the dynamic inertia weight, the improved cuckoo search updates the next nest position based on the former best nest position that has been saved with dynamic inertia weight, which can well balance the relation between population exploration and development capabilities. This paper also has a convergence analysis of the improved cuckoo search by the characteristic equation. The performance of the new method is compared with the basic cuckoo search, particle swarm optimization, ant colony optimization and other algorithms, showing that the improved cuckoo search algorithm can significantly reduce the number of iterations and running time, and can effectively improve the convergence speed and convergence precision.

参考文献/References:

[1] YANG Xinshe, DEB S. Cuckoo search via Lévy flights[C]//World Congress on Nature & Biologically Inspired Computing. Coimbatore, India, 2009: 210-214.
[2] 李煜, 马良. 新型元启发式布谷鸟搜索算法[J]. 系统工程, 2012, 30(8): 64-69. LI Yu, MA Liang. A new metaheuristic cuckoo search algorithm[J]. Systems Engineering, 2012, 30(8): 64-69.
[3] 陈乐, 龙文. 求解工程结构优化问题的改进布谷鸟搜索算法[J]. 计算机应用研究, 2014, 31(3): 679-683. CHEN Le, LONG Wen. Modified cuckoo search algorithm for solving engineering structural optimization problem[J]. Application Research of Computers, 2014, 31(3): 679-683.
[4] OUAARAB A, AHIOD B, YANG Xinshe. Discrete cuckoo search algorithm for the travelling salesman problem[J]. Neural Computing and Applications, 2014, 24(7/8): 1659-1669.
[5] SETHI R, PANDA S, SAHOO B P. Cuckoo search algorithm based optimal tuning of PID structured TCSC controller[M]//JAIN L C, BEHERA H S, MANDAL J K, et al. Computational Intelligence in Data Mining-Volume 1. Odisha: Springer, 2015: 251-263.
[6] WALTON S, HASSAN O, MORGAN K, et al. Modified cuckoo search: a new gradient free optimisation algorithm[J]. Chaos, Solitons and Fractals, 2011, 44(9): 710-718.
[7] ZHENG Hongqing, ZHOU Yongquan. A novel cuckoo search optimization algorithm based on Gauss distribution[J]. Journal of Computational Information Systems, 2012, 8(10): 4193-4200.
[8] 苏芙华, 刘云连, 伍铁斌. 求解无约束优化问题的改进布谷鸟搜索算法[J]. 计算机工程, 2014, 40(5): 224-227, 233. SU Fuhua, LIU Yunlian, WU Tiebin. Modified cuckoo search algorithm for solving unconstrained optimization problem[J]. Computer Engineering, 2014, 40(5): 224-227, 233.
[9] 龙文, 陈乐. 求解约束化工优化问题的混合布谷鸟搜索算法[J]. 计算机应用, 2014, 34(2): 523-527. LONG Wen, CHEN Le. Hybrid cuckoo search algorithm for solving constrained chemical engineering optimization problems[J]. Journal of Computer Applications, 2014, 34(2): 523-527.
[10] VISWANATHAN G M, AFANASYEV V, BULDYREV S V, et al. Lévy flights in random searches[J]. Physica A:Statistical Mechanics and its Applications, 2000, 282(1/2): 1-12.
[11] 邓鑫洋, 邓勇, 章雅娟, 等. 一种信度马尔科夫模型及应用[J]. 自动化学报, 2012, 38(4): 666-672. DENG Xinyang, DENG Yong, ZHANG Yajuan, et al. A belief Markov model and its application[J]. Acta Automatica Sinica, 2012, 38(4): 666-672.
[12] SHI Yuhui, EBERHART R. A modified particle swarm optimizer[C]//IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference on Evolutionary Computation Proceedings. Anchorage, USA, 1998: 69-73.
[13] SHI Yuhui, EBERHART R C. Empirical study of particle swarm optimization[C]//Proceedings of the 1999 Congress on Evolutionary Computation. Washington DC, USA, 1999, 3: 1945-1949.
[14] PERAM T, VEERAMACHANENI K, MOHAN C K. Fitness-distance-ratio based particle swarmoptimization[C]//Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Indianapolis, USA, 2003: 174-181.
[15] SHI Yuhui, EBERHART R C. Fuzzy adaptive particle swarm optimization[C]//Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, Korea, 2001: 101-106.
[16] EBERHART R C, SHI Yuhui. Tracking and optimizing dynamic systems with particle swarms[C]//Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, Korea, 2001: 94-100.
[17] ZHANG Liping, YU Huanjun, HU Shangxu. A new approach to improve particle swarm Optimization[C]//Genetic and Evolutionary Computation—GECCO 2003. Berlin Heidelberg, Germany, 2003: 134-139.
[18] 王俊伟, 汪定伟. 粒子群算法中惯性权重的实验与分析[J]. 系统工程学报, 2005, 20(2): 194-198. WANG Junwei, WANG Dingwei. Experiments and analysis on inertia weight in particle swarm optimization[J]. Journal of Systems Engineering, 2005, 20(2): 194-198.
[19] 张永韡, 汪镭, 吴启迪. 动态适应布谷鸟搜索算法[J]. 控制与决策, 2014, 29(4): 617-622. ZHANG Yongwei, WANG Lei, WU Qidi. Dynamic adaptation cuckoo search algorithm[J]. Control and Decision, 2014, 29(4): 617-622.
[20] 王凡, 贺兴时, 王燕, 等. 基于 CS 算法的Markov 模型及收敛性分析[J]. 计算机工程, 2012, 38(11): 180-182, 185. WANG Fan, HE Xingshi, WANG Yan, et al. Markov model and convergence analysis based on cuckoo search algorithm[J]. Computer Engineering, 2012, 38(11): 180-182, 185.
[21] 李枝勇, 马良, 张惠珍. 蝙蝠算法收敛性分析[J]. 数学的实践与认识, 2013, 43(12): 182-190. LI Zhiyong, MA Liang, ZHANG Huizhen. Convergence analysis of bat algorithm[J]. Mathematics in Practice and Theory, 2013, 43(12): 182-190.
[22] 刘洪波, 王秀坤, 谭国真. 粒子群优化算法的收敛性分析及其混沌改进算法[J]. 控制与决策, 2006, 21(6): 636-640. LIU Hongbo, WANG Xiukun, TAN Guozhen. Convergence analysis of particle swarm optimization and its improved algorithm based on chaos[J]. Control and Decision, 2006, 21(6): 636-640.
[23] EBERHART R C, SHI Y. Comparing inertia weights and constriction factors in particle swarm optimization[C]//Proceedings of the 2000 Congress on Evolutionary Computation. La Jolla, Germany, 2000: 84-88.
[24] 马良. 基于蚂蚁算法的函数优化[J]. 控制与决 策, 2002, 17(增刊): 719-722. MA Liang. Ant algorithm based function optimization[J]. Control and Decision, 2002, 17(S): 719-722.
[25] 李枝勇, 马良, 张惠珍. 多目标 0-1 规划问题的蝙蝠算法[J]. 智能系统学报, 2014, 9(6): 672 -676. LI Zhiyong, MA Liang, ZHANG Huizhen. Bat algorithm for the multi-objective 0-1 programming problem[J]. CAAI Transactions on Intelligent Systems, 2014, 9(6): 672-676.

相似文献/References:

[1]陈明杰,黄佰川,张旻.混合改进蚁群算法的函数优化[J].智能系统学报编辑部,2012,7(04):370.
 CHEN Mingjie,HUANG Baichuan,ZHANG Min.Function optimization based on an improved hybrid ACO[J].CAAI Transactions on Intelligent Systems,2012,7(04):370.
[2]刘长平,叶春明.具有Lévy飞行特征的蝙蝠算法[J].智能系统学报编辑部,2013,8(03):240.
 LIU Changping,YE Chunming.Bat algorithm with the characteristics of Lévy flights[J].CAAI Transactions on Intelligent Systems,2013,8(04):240.
[3]莫愿斌,马彦追,郑巧燕,等.单纯形法的改进萤火虫算法及其在非线性方程组求解中的应用[J].智能系统学报编辑部,2014,9(06):747.[doi:10.3969/j.issn.1673-4785.201309075]
 MO Yuanbin,MA Yanzhui,ZHENG Qiaoyan,et al.Improved firefly algorithm based on simplex method and its application in solving non-linear equation groups[J].CAAI Transactions on Intelligent Systems,2014,9(04):747.[doi:10.3969/j.issn.1673-4785.201309075]
[4]陈杰,沈艳霞,陆欣.基于信息反馈和改进适应度评价的人工蜂群算法[J].智能系统学报编辑部,2016,11(2):172.[doi:10.11992/tis.201506024]
 CHEN Jie,SHEN Yanxia,LU Xin.Artificial bee colony algorithm based on information feedback and an improved fitness value evaluation[J].CAAI Transactions on Intelligent Systems,2016,11(04):172.[doi:10.11992/tis.201506024]

备注/Memo

备注/Memo:
收稿日期:2014-09-30;改回日期:。
基金项目:河南省科技攻关重点基金资助项目(122102210201);河南大学研究生教育综合改革基金资助项目(Y1427056).
作者简介:周欢,1990年生,女,硕士研究生,主要研究方向为智能优化、电子商务;李煜,1969年生,女,教授,博士,主要研究方向为智能优化、电子商务、物流管理。
通讯作者:李煜.E-mail:lyhenu@163.com.
更新日期/Last Update: 2015-08-28