Equilibrium Single Evolution Based Cuckoo Search Algorithm

FU Wen-yuan

doi:10.3969/j.issn.0372-2112.2019.02.004

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Equilibrium Single Evolution Based Cuckoo Search Algorithm

更新时间：2025-07-16

- Equilibrium Single Evolution Based Cuckoo Search Algorithm
- Acta Electronica Sinica Vol. 47, Issue 2, Pages: 282-288(2019)
- 作者机构：
  
  1. 华侨大学信息科学与工程学院,福建,厦门,361002
  2. 中山大学电子与信息工程学院,广东,广州,510006
  3. 厦门市专用电路系统重点实验室,福建,厦门,361008
  4. 福建省电机控制与系统优化调度工程技术研究中心,福建,厦门,361002
  5. 华侨大学信息科学与工程学院,福建,厦门,361002
  6. 中山大学电子与信息工程学院,广东,广州,510006
  7. 厦门市专用电路系统重点实验室,福建,厦门,361008
  8. 福建省电机控制与系统优化调度工程技术研究中心,福建,厦门,361002
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.02.004
  CLC： TP18
- Published Online：25 February 2019，
  
  Published：2019
- 稿件说明：
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FU Wen-yuan. Equilibrium Single Evolution Based Cuckoo Search Algorithm[J]. Acta Electronica Sinica, 2019, 47(2): 282-288.
DOI：

FU Wen-yuan. Equilibrium Single Evolution Based Cuckoo Search Algorithm[J]. Acta Electronica Sinica, 2019, 47(2): 282-288. DOI： 10.3969/j.issn.0372-2112.2019.02.004.

摘要

针对布谷鸟算法采用整体评价策略处理多维度自变量相关优化问题时，维度耦合现象会恶化算法的搜索速度和收敛精度，提出均衡单进化的布谷鸟算法（ESCES）.该算法给出一种新型的均衡单进化函数评价策略，即每一代进化只随机更新目标函数的单个维度，并且随机更新的维度服从均匀分布，避免多维度之间互相干扰.同时，提出两种新型随机游动步长更新学习律，提高了优化算法的全局搜索速度和收敛精度.实验测试结果和显著性统计结果表明，ESCES算法与5个改进CS算法及7个其它最新智能优化算法相比，在全局寻优性能、搜索速度和收敛精度上均获得较大的改进.

Abstract

For the whole evaluation strategy in cuckoo search algorithm in the face of multi-dimension function optimization problems

the coupling phenomena among dimensions will deteriorate the search speed and convergence accuracy.Therefore

a new cuckoo search algorithm based on the equilibrium single evolution mechanism is proposed.Then

a new equilibrium single evolution evaluation strategy is also used to update randomly the single dimension of the objective function on each iteration.Note that the randomly updated dimensions obey the uniform distribution to avoid mutual interference between dimensions.Furthermore

two new random walking update laws are proposed to improve the global search speed and convergence accuracy.The results of the 10 benchmark functions and statistical significance demonstrate that ESCES algorithm has a great improvement in global optimization performance

search speed and convergence accuracy compared with the five modified CS algorithms and seven other state-of-the art algorithms.

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