Grey Wolf Optimizer Based on Tent Chaotic Sequence

ZHANG Jia-kui; CUI Li-jie; GUO Qing; CHEN Hao-ran

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Grey Wolf Optimizer Based on Tent Chaotic Sequence

更新时间：2026-09-04

- Grey Wolf Optimizer Based on Tent Chaotic Sequence
- Vol. 35, Issue 6, Pages: 11-16(2018)
- 作者机构：
  
  空军工程大学机构装备管理与安全工程学院,陕西,西安,710051
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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张贾奎, 崔利杰, 郭庆, 陈浩然. 基于Tent混沌序列的灰狼优化算法[J]. 微电子学与计算机, 2018, 35(6): 11-16.

ZHANG Jia-kui, CUI Li-jie, GUO Qing, et al. Grey Wolf Optimizer Based on Tent Chaotic Sequence[J]. 2018, 35(6): 11-16.
张贾奎, 崔利杰, 郭庆, 陈浩然. 基于Tent混沌序列的灰狼优化算法[J]. 微电子学与计算机, 2018, 35(6): 11-16. DOI：

ZHANG Jia-kui, CUI Li-jie, GUO Qing, et al. Grey Wolf Optimizer Based on Tent Chaotic Sequence[J]. 2018, 35(6): 11-16. DOI：

摘要

针对灰狼优化(GWO)算法在优化求解过程中存在的局部搜索能力差、易陷入局部最优等问题

提出了基于Tent混沌序列的局部搜索策略以及多样性维持策略.由此构建了基于Tent混沌序列的灰狼优化算法(TCGWO).所提出的策略分别用以提高算法的局部搜索能力、避免算法陷入局部最优.通过对23个测试函数的仿真计算

并与GWO以及FPA、ABC等进行综合对比

结果验证了TCGWO算法的有效性及优越性.

Abstract

Focusing on the disadvantages that Grey Wolf Optimizer (GWO) was easy to fall into local optimum and had bad local searching ability on optimization

local search strategy and diversity maintenance strategy were proposed. On the basis of that

the Grey Wolf Optimizer Based on Tent Chaotic Sequence (TCGWO) was established. The local searching ability could be improved by local search strategy; falling into local optimum could be avoided by diversity maintenance strategy. 23 benchmark test functions were used to experiment and analysis

and comparative analysis with GWO

Flower Pollination Algorithm (FPA) and Artificial Bee Colony Algorithm (ABC). According to the result

TCGWO is effective and superior.

关键词

Keywords

references

New York Haupt R, Haupt S. Practical genetic algorithm[M]. New York: John Wiley and Sons, 2004:38-39. DOI: 38

Muangkote N, Sunat K, Chiewchanwattana S. An im-proved grey wolf optimizer for training q-Gaussian radial basis func-tional-link nets[C]//Proceedings of the 2014 International Computer Science and Engineering Conference. Piscataway, NJ: IEEE, 2014: 209-214.

Zhu A, Xu C, Li Z, et al. Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC[J]. Journal of Systems Engineering and Electronics, 2015, 26(2):317-328. DOI: 317

Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69(3): 46-61. DOI: 46

魏政磊, 赵辉, 韩邦杰, 等.基于自适应GWO的多UCAV协同攻击目标决策[J].计算机工程与应用, 2016, 52(18):257-261. DOI: 257

单梁, 强浩, 李军, 等基于Tent映射的混沌优化算法[J].控制与决策, 2005, 20(2):179-182. DOI: 179

魏政磊, 赵辉, 李牧东, 等.控制参数值非线性调整策略的灰狼优化算法[J].空军工程大学学报(自然科学版), 2016, 17(3):68-72. DOI: 68

姚鹏, 王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策, 2016, 31(4):701-708. DOI: 701

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