[1]梁毛毛,肖 文,王李进,等.带全局-局部最优步长比例因子的布谷鸟搜索算法[J].南京师范大学学报(工程技术版),2022,22(02):056-62.[doi:10.3969/j.issn.1672-1292.2022.02.009]
　Liang Maomao,Xiao Wen,Wang Lijin,et al.Cuckoo Search Algorithm with Global-Local Best Scaling Factor[J].Journal of Nanjing Normal University(Engineering and Technology),2022,22(02):056-62.[doi:10.3969/j.issn.1672-1292.2022.02.009]
点击复制

带全局-局部最优步长比例因子的布谷鸟搜索算法
分享到：

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
22卷
期数:
2022年02期
页码:
056-62
栏目:
计算机科学与技术
出版日期:
2022-06-30

文章信息/Info

Title:
Cuckoo Search Algorithm with Global-Local Best Scaling Factor
文章编号:
1672-1292(2022)02-0056-07
作者:
梁毛毛1; 2; 肖 文1; 2; 王李进1; 2; 钟一文1; 2
(1.福建农林大学计算机与信息学院,福建 福州 350002)(2.福建农林大学智慧农林福建省高校重点实验室,福建 福州 350002)
Author(s):
Liang Maomao1; 2; Xiao Wen1; 2; Wang Lijin1; 2; Zhong Yiwen1; 2
(1.College of Computer and Information Sciences,Fujian Agriculture and Forestry University,Fuzhou 350002,China)(2.Key Laboratory of Smart Agriculture and Forestry,Fujian Agriculture and Forestry University,Fuzhou 350002,China)
关键词:
布谷鸟搜索算法; 全局-局部最优适应值; 比例因子; 可变因子; 函数优化
Keywords:
cuckoo search algorithm; global-local best fitness; scaling factor; varied factor; function optimization
分类号:
O643; X703
DOI:
10.3969/j.issn.1672-1292.2022.02.009
文献标志码:
A
摘要:
布谷鸟搜索算法利用Lévy Flights随机走动和Biased随机走动过程完成全局搜索和局部开发. 针对原始的Lévy Flights随机走动仅采用固定的常数步长因子,介绍了一种使用每一代中个体的全局和局部最优适应值动态设置步长因子的方法,并提出了一种带全局-局部最优步长比例因子的布谷鸟搜索算法. 在测试函数上的运行结果证明,该方法是可行的,且能够全面有效地加强布谷鸟搜索算法的收敛速度和求精能力,其性能总体上比采用固定因子、基于均匀分布随机数或基于贝塔分布随机数比例因子的布谷鸟搜索算法更优.
Abstract:
Lévy Flights random walk and Biased/selective random walk is employed in cuckoo search algorithm to search for new solutions. Instead of a fixed scaling factor in standard Lévy Flights,in our study,global and local best fitness of individuals in each generation are utilized to dynamically define the scaling factor. And a modified cuckoo search algorithm,called GlbestCS is proposed. Comprehensive experiments demonstrate that this strategy is feasible,and it can effectively strengthen the convergence speed and improve accuracy of cuckoo search algorithm. In addition,the performance of the proposed algorithm is generally better than that of cuckoo search algorithm that uses a constant scaling factor,either based on uniformly distributed random numbers or based on a beta distribution random numbers.

参考文献/References:

[1] 王永安,赵阳,蓝雨晨,等. 基于进化差分算法的环形电感建模及应用[J]. 南京师范大学学报(工程技术版),2020,20(3):32-37.
[2]王姝,张海龙,王恩荣. 基于改进粒子群算法的配电网网架优化研究[J]. 南京师范大学学报(工程技术版),2020,20(1):15-19.
[3]沈燕平,鞠勇,居茜. 考虑间歇性负荷的光储微网储能的优化配置技术研究[J]. 南京师范大学学报(工程技术版),2020,20(2):39-43,92.
[4]赵世田,付莹莹,曾勇,等. 基于非线性自适应度函数的遗传算法求取自由曲面最大主曲率研究[J]. 南京师范大学学报(工程技术版),2018,18(3):19-24.
[5]YANG X S,DEB S. Cuckoo search via Lévy flights[C]//Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing(NaBIC’09). Coimbatore,India:IEEE,2009.
[6]YANG X S,DEB S. Engineering optimisation by cuckoo search[J]. International Journal of Mathematical Modelling and Numerical Optimisation,2010,1(4):330-343.
[7]WANG G G,GANDOMI A H,ZHAO X,et al. Hybridizing harmony search algorithm with cuckoo search for global numerical optimization[J]. Soft Computing,2016,20(1):273-285.
[8]ROSLI S J,RAHIM H A,ABDUL R K N,et al. A hybrid modified method of the sine cosine algorithm using latin hypercube sampling with the cuckoo search algorithm for optimization problems[J]. Electronics,2020,9(11):1786.
[9]SHEHAB M,KHADER A T,LAOUCHEDI M. A hybrid method based on cuckoo search algorithm for global optimization problems[J]. Journal of Information and Communication Technology,2018,17(3):469-491.
[10]SHEHAB M,KHADER A T,LAOUCHEDI M,et al. Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization[J]. Journal of Supercomputing,2019,75(5):2395-2422.
[11]LI X T,WANG J N,YIN M H. Enhancing the performance of cuckoo search algorithm using orthogonal learning method[J]. Neural Computing and Applications,2014,24(6):1233-1247.
[12]WANG L J,ZHONG Y W.One-position inheritance based cuckoo search algorithm[J]. International Journal of Computing Science and Mathematics,2015,6(6):546-554.
[13]WANG L J,YIN Y L,ZHONG Y W. Cuckoo search with varied scaling factor[J]. Frontiers of Computer Science,2015,9(4):623-635.
[14]王李进,尹义龙,钟一文. 逐维改进的布谷鸟搜索算法[J]. 软件学报,2013,24(11):2687-2698.
[15]王李进,钟一文,尹义龙. 带外部存档的正交交叉布谷鸟搜索算法[J]. 计算机研究与发展,2015,52(11):2496-2507.
[16]SALGOTRA R,SINGH U,SAHA S,et al. Self adaptive cuckoo search:analysis and experimentation[J]. Swarm and Evolutionary Computation,2021,60:100751.
[17]WEI J M,YU Y G. A novel cuckoo search algorithm under adaptive parameter control for global numerical optimization[J]. Soft Computing,2019,24:4917-4940.
[18]WANG K,LIAN X F,PAN B. Improved cuckoo search algorithm based on exponential function[C]//Proceedings of the 2019 Chinese Intelligent Automation Conference. Zhenjiang,China:Springer,2019.
[19]CHEN L,LU H Q,LI H W,et al. Dimension-by-dimension enhanced cuckoo search algorithm for global optimization[J]. Soft Computing,2019,23(21):11297-11312.
[20]林要华,梁忠,胡华平. 贝塔分布的布谷鸟搜索算法[J]. 南京大学学报(自然科学版),2016,52(4):638-646.
[21]WALTON S,HASSAN O,MORGAN K,et al. Modified cuckoo search:a new gradient free optimisation algorithm[J]. Chaos,Solitons & Fractals,2011,44(9):710-718.
[22]VALIAN E,TAVAKOLI S,MOHANNA S,et al. Improved cuckoo search for reliability optimization problems[J]. Computers & Industrial Engineering,2013,64(1):459-468.
[23]LAYEB A.A novel quantum inspired cuckoo search for knapsack problems[J]. International Journal of Bio-inspired Computation,2011,3(5):297-305.
[24]ARUMUGAM M S,RAO M V C. On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems[J]. Discrete Dynamics in Nature and Society,2006(3):79295.
[25]NOMAN N,IBA H. Accelerating differential evolution using an adaptive local search[J]. IEEE Transactions on Evolutionary Computation,2008,12(1):107-125.
[26]GARCíA S,MOLINA D,LOZANO M,et al. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour:a case study on the CEC’2005 special session on real parameter optimization[J]. Journal of Heuristics,2009,15(6):617-644.
[27]GONG W Y,CAI Z H. Differential evolution with ranking-based mutation operators[J]. IEEE Transactions on Cybernetics,2013,43(6):2066-2081.
[28]WANG L J,ZHONG Y W. Cuckoo search algorithm with chaotic maps[J]. Mathematical Problems in Engineering,2015(1):715635.

备注/Memo

备注/Memo:
收稿日期:2021-08-31.
基金项目:福建省自然科学基金项目(2021J01127)、福建农林大学科技创新专项基金项目(CXZX2020148C、CXZX2020150C)、数字福建旅游大数据研究所开放基金项目(DFJTBDRI2020103)..
通讯作者:王李进,博士,教授,研究方向:仿生优化算法及其应用. E-mail:lijinwang@fafu.edu.cn
更新日期/Last Update: 1900-01-01