The Study of Improved Chicken Swarm Optimization Algorithm based on Simulated Annealing

LI Zhen-bi; WANG Kang; JIANG Yuan-yuan

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The Study of Improved Chicken Swarm Optimization Algorithm based on Simulated Annealing

更新时间：2026-09-04

- The Study of Improved Chicken Swarm Optimization Algorithm based on Simulated Annealing
- Vol. 34, Issue 2, Pages: 31-33(2017)
- 作者机构：
  
  安徽理工大学机构电气与信息工程学院,安徽,淮南,232001
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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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): 31-33, 38.

LI Zhen-bi, WANG Kang, JIANG Yuan-yuan. The Study of Improved Chicken Swarm Optimization Algorithm based on Simulated Annealing[J]. 2017, 34(2): 31-33.
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): 31-33, 38. DOI：

LI Zhen-bi, WANG Kang, JIANG Yuan-yuan. The Study of Improved Chicken Swarm Optimization Algorithm based on Simulated Annealing[J]. 2017, 34(2): 31-33. DOI：

摘要

针对鸡群算法因小鸡粒子易陷入局部最优而无法取得全局最优解问题

在小鸡粒子位置更新中加入自身惯性权重和向子群中公鸡粒子学习部分

提出具有随机惯性权重和固定学习因子的改进鸡群算法

然后用模拟退火算法对改进鸡群算法陷入停滞状态时已得到的最优解进行邻域搜索

使算法具有跳出局部最优取得全局最优解的能力

最后将基于模拟退火的改进鸡群算法用于4个标准测试函数寻优.仿真结果表明

基于模拟退火的改进鸡群算法全局搜索能力强

收敛速度快

精度高

与粒子群算法、鸡群算法以及改进鸡群算法相比寻优性能更佳.

Abstract

The part of chicks learning from the rooster in their subgroup is added to chicks position update equation

and the inertia weight and learning factor are introduced

put forward an improved chicken swarm optimization algorithm with the random inertia weight and fixed learning factors to deal with the chicken particles into local optimum easily and cant obtain the global optimal solution in the chicken swarm optimization algorithm

then using simulated annealing algorithm search in the field for the optimal solution when chicken swarm optimization to a standstill

makes the algorithm has the capacity of jumping out of local optimal to obtain the global optimal solution

finally

the improved chicken swarm optimization algorithm based on simulated annealing was tested by four classic functions. The simulation results show that the proposed algorithm have strong capability of global search

high convergence precision and fast convergence speed

the proposed algorithm whose performance of global searching was superior to particle swarm optimization

chicken swarm optimization and improved chicken swarm optimization.

关键词

Keywords

references

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Sarah Lyden, Md Enamul Haque. A simulated annealing global maximum power point tracking approach for PV modules under partial shading conditions[J]. IEEE Transactions on Power Electronics, 2016, 31(06): 4171-4181. DOI: 4171

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刘春波. 混合模拟退火算法的研究及其在电机优化设计中的应用[D]. 广州: 中山大学, 2010.

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Meng Xianbing, Liu Yu, Gao Xiaozhi, 用. A new bio-inspired algorithm: chicken swarm optimization[J]. Lecture Notes in Computer Science, 2014(8794): 86-94. DOI: 86

林蔚天, 许伟, 顾幸生. 基于随机学习因子混沌PSO算法的甲醇合成转化率测量[J]. 化工学报, 2012, 63(9): 2899-2903. DOI: 2899

Jeong Jung Kim; Ju Jang Lee. Trajectory optimization with particle swarm optimization for manipulator motion planning[J]. IEEE Transactions on Industrial Informatics, 2015, 11(3): 620-631. DOI: 620

吴进华, 吴华丽, 周仕. 基于模拟退火的粒子群优化算法[J]. 仪器仪表学报, 2008, 29(8): 50-53. DOI: 50

李田来, 刘方爱, 王新华. 基于分治策略的人工蜂群算法[J]. 控制与决策, 2015, 30(2): 316-320. DOI: 316

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