具有记忆性的自适应布谷鸟搜索算法

秦岭; 戴睿闻

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具有记忆性的自适应布谷鸟搜索算法

更新时间：2026-09-04

- 具有记忆性的自适应布谷鸟搜索算法
- Adaptive Cuckoo Search Algorithm with Memory Strategy
- 微电子学与计算机 2017年34卷第3期页码：15-19
- 作者机构：
  
  南京工业大学机构计算机科学与技术学院,江苏,南京,211816
- 作者简介：
- 基金信息：
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- 纸质出版：2017
- 稿件说明：
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秦岭, 戴睿闻. 具有记忆性的自适应布谷鸟搜索算法[J]. 微电子学与计算机, 2017, 34(3): 15-19, 24.

QIN Ling, DAI Rui-wen. Adaptive Cuckoo Search Algorithm with Memory Strategy[J]. 2017, 34(3): 15-19.
秦岭, 戴睿闻. 具有记忆性的自适应布谷鸟搜索算法[J]. 微电子学与计算机, 2017, 34(3): 15-19, 24. DOI：

QIN Ling, DAI Rui-wen. Adaptive Cuckoo Search Algorithm with Memory Strategy[J]. 2017, 34(3): 15-19. DOI：

摘要

针对标准布谷鸟搜索算法后期收敛速度慢、易陷入局部最优等缺点

本文提出了一种具有记忆性的自适应布谷鸟算法(MACS).根据种群适应度与个体适应度准确判定算法收敛程度并分别对不同鸟窝进行自适应调整

使算法在迭代过程中保持收敛速度与精度的平衡; 通过在偏好随机游动环节中引入记忆策略

算法能够充分利用上一代的有效信息

提高了其跳出局部最优的能力.仿真实验结果表明

MACS算法各方面性能较标准算法及其改进版本具有明显优势.

Abstract

The cuckoo search algorithm (CSA) is a new nature-inspired algorithm which is simple and efficient. This algorithm uses Lévy flights random walk and biased random walk to search for new solutions. Aiming at increasing the performance of cuckoo search algorithm via improving parameter

this paper presents Adaptive Cuckoo Search Algorithm with Memory Strategy(MACS). By adjusting Lévy flight adaptively based on premature convergence degree and the fitness of the cuckoo

the algorithm makes a compromise between global search and local search.The ability of local search has improved via introducing memory strategy.Simulation results have showed that the performance of MACS is obviously improved in the aspect of the convergence and accuracy compared with the CS algorithm and other improved editions.

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references

Yang X S, Deb S. Cuckoo search via Levy flights[C]// Proceedings of World Congress on Nature % Biologically Inspired Computing, NaBIC 2009. Washington: IEEE Publications, 2009: 210-214.

Aziz Ouaarab, Belaï Ahiod, Xinshe Yang. Random-key cuckoo search for the traveling salesman problem[J]. Soft Computing, 2015, 19(4): 1099-1106. [doi:10.1007/s00500-014-1322-9].

Wang Lijin, Yin Yilong, Zhong Yiwen. Cuckoo search with varied scaling factor[J]. Front. Comput, 2015, 9(4): 623-635. DOI: 623

Yang X S, Deb S. Engineering optimization by cuckoo search[J]. Int'l Journal of Mathematical Modeling and Numerical Optimization, 2010, 1(4): 330-343. [doi: 10.1504/IJMMNO.2010.03543].

UK Yang X S. Nature-inspired metaheuristic algorithms[M]. UK: Luniver Press, 2008: 81-96. DOI: 81

Yang X S, Deb S. cuckoo search: recent advances and applications[J]. Neural Computing and Applications, 2014, 24(1): 169-174. DOI: 169

王李进, 尹义龙. 逐维改进的布谷鸟搜索算法[J]. 软件学报, 2013, 24(11): 2687-2698. DOI: 2687

Walton S, Hassan O, Morgan K, et al. Modified cuckoo search: A new gradient free optimization algorithm[J]. Chaos, Solutions % Fractals, 2011, 44(9): 710-718. DOI: 710

Valian E, Mohanna S, Tavakoli S. Improved cuckoo search algorithm for global optimization[J]. Int'l Journal of Communications and Information Technology, 2011, 1(1): 31-44. DOI: 31

Jin Qibing, Qi Linfeng. Novel improved cuckoo search for PID controller design[J]. Transactions of the Institute of Measurement and Control, 2014, 37(6): 1-11. DOI: 1

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