An Improved Chicken Algorithm Based on Positive Learning and Reverse Learning

ZHANG Mu-xue; ZHANG Da-min; YANG Ju-qing; ZHUCHEN Rou-ling

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An Improved Chicken Algorithm Based on Positive Learning and Reverse Learning

更新时间：2026-09-04

- An Improved Chicken Algorithm Based on Positive Learning and Reverse Learning
- Vol. 35, Issue 6, Pages: 22-27(2018)
- 作者机构：
  
  贵州大学大数据与信息工程学院,贵州,贵阳,550025
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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张慕雪, 张达敏, 杨菊蜻, 朱陈柔玲. 一种基于正向学习和反向学习的改进鸡群算法[J]. 微电子学与计算机, 2018, 35(6): 22-27.

ZHANG Mu-xue, ZHANG Da-min, YANG Ju-qing, et al. An Improved Chicken Algorithm Based on Positive Learning and Reverse Learning[J]. 2018, 35(6): 22-27.
张慕雪, 张达敏, 杨菊蜻, 朱陈柔玲. 一种基于正向学习和反向学习的改进鸡群算法[J]. 微电子学与计算机, 2018, 35(6): 22-27. DOI：

ZHANG Mu-xue, ZHANG Da-min, YANG Ju-qing, et al. An Improved Chicken Algorithm Based on Positive Learning and Reverse Learning[J]. 2018, 35(6): 22-27. DOI：

摘要

针对标准的鸡群算法容易陷入局部最优的缺陷

提出一种基于正向学习和反向学习的改进鸡群算法.公鸡粒子在每次迭代中向最优粒子正向学习

使算法迅速进入最有希望的区域寻找食物; 而在算法陷入局部最优解时向最差粒子反向学习以跳出局部最优.通过对6个典型的基准测试函数的仿真表明

改进算法具有较强的全局搜索能力

同时寻优精度和收敛速度比原算法也有较大的提高.尤其是在处理高维函数问题上

改进算法表现出了较强的优势.

Abstract

A Chicken Swarm Optimization with Positive Learning and Reverse Learning(PRCSO) was proposed to solve the defect that traditional Chicken Swarm Optimization(CSO) is easy to fall into local optimum. The cock crow always learns the optimal particles in each iteration

so that the algorithm can quickly enter the optimal region to find food; and when the algorithm falls into the local optimal solution

Learn to jump out of the local optimal. The results on six typical standard test functions show that the improved CSO not only improves the global search ability

but also the search efficiency

search accuracy and convergence rate are better than the traditional CSO. Especially in dealing with high-dimensional function problems

the improved HJCSO shows a strong advantage.

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Keywords

references

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Chen Y L, He P L, Zhang Y H. Combining penalty function with modified chicken swarm optimization for constrained optimization[C]// First International Conference on Information Sciences, Machinery, Materials and Energy. Pairs: Atlantis Press, 2015: 1899-1907.

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孔飞, 吴定会.一种改进的鸡群算法[J].江南大学学报(自然科学版), 2015, 14(6):681-688. DOI: 681

肖亮, 刘思彤.基于认知多样性变异的鸡群算法协同优化异步实现[J].计算机科学, 2017, 44(6A):99-104. DOI: 99

李俊, 汪冲, 李波, 等.基于扰动的精英反向学习粒子群优化算法[J].计算机应用研究, 2016, 33(9):2584-2587. DOI: 2584

李振璧, 王康, 姜媛媛.基于模拟退火的改进鸡群优化算法[J].微电子学与计算机, 2017, 34(2):30-33. DOI: 30

张建春, 康凤举, 梁洪涛, 等.基于鸡群优化的粒子滤波算法研究[J].系统仿真学报, 2017, 29(2):295-300. DOI: 295

徐仪勋, 李旺, 李东东, 等.基于改进鸡群算法的非侵入式家电负荷分解[J].电力系统保护与控制, 2016, 44(13):27-32. DOI: 27

崔东文.鸡群优化算法-投影寻踪洪旱灾害评估模型[J].水利水电科技进展, 2016, 36(2):16-23. DOI: 16

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