计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 270-280.doi: 10.11896/jsjkx.200800087

• 人工智能 • 上一篇    下一篇

基于生成时间序列均匀优化的混沌人工蜂群算法

石克翔, 保利勇, 丁洪伟, 官铮, 赵雷   

  1. 云南大学信息学院 昆明650500
  • 收稿日期:2020-08-14 修回日期:2020-11-05 出版日期:2021-07-15 发布日期:2021-07-02
  • 通讯作者: 保利勇(bly.yx@163.com)
  • 基金资助:
    国家自然科学基金项目(61461053,61761045)

Chaos Artificial Bee Colony Algorithm Based on Homogenizing Optimization of Generated Time Series

SHI Ke-xiang, BAO Li-yong, DING Hong-wei, GUAN Zheng, ZHAO Lei   

  1. School of Information,Yunnan University,Kunming 650500,China
  • Received:2020-08-14 Revised:2020-11-05 Online:2021-07-15 Published:2021-07-02
  • About author:SHI Ke-xiang,born in 1995,postgra-duate.His main research interests include nonlinear science,chaos theory and control,group intelligence algorithm.(1677293970@qq.com)
    BAO Li-yong,born in 1975,Ph.D,associate professor.His main research in-terests include computer communication network,chaotic spread spectrum communication and network security.
  • Supported by:
    National Natural Science Foundation of China(61461053,61761045).

摘要: 为了优化与初始蜜源和搜索方式相关的时间序列分布,从而提高算法全局开拓性和遍历寻优效率,提出一种基于生成时间序列均匀优化的混沌人工蜂群算法。针对混沌时间序列生成的初始蜜源的分布过于集中的问题,首先依据最大熵原理,对Logistic混沌映射进行均匀化优化,并运用熵谱分析以及NIST随机性测试的方法验证其随机程度,使得由其生成的初始蜜源可以随机且均匀地分布在整个解空间,为算法的全局寻优奠定基础;其次,依据由近及远的搜索策略,改进邻域搜索方式,利用均匀化时间序列查找蜜源最优位置,提高算法的遍历速度和收敛精度;最后,所提算法对9个标准测试函数进行了实验仿真,并与其他改进人工蜂群算法和优化算法从收敛曲线和寻优结果上进行了综合比较,进而将6种算法合理引入到物流配送问题中以寻找最短路径。实验结果表明,所提优化算法不仅加强了初始蜜源的均匀性,而且优化效果更为显著,能跳出局部最优且能准确快速地找到全局最优解。

关键词: Logistic均匀化, 混沌人工蜂群算法, 邻域重构, 蜜源分布优化, 熵谱分析

Abstract: In order to optimize the distribution of the time series related to the initial honey source and search method,and further improve the algorithm’s global pioneering and traversal optimization efficiency,a chaos artificial bee colony algorithm based on homogenizing optimization of generated time series is proposed in this paper.Aiming at the problem that the distribution of initial honey sources generated by chaos time series is too concentrated,firstly,based on the principle of maximum entropy,the Logistic chaos mapping is optimized for homogenization,and entropy spectrum analysis and NIST randomness test are used to verify the randomness of the generated time series,so that the initial honey source generated by it can be randomly and uniformly distributed in the entire solution space,which lays the foundation for the global optimization of the algorithm.Secondly,this paper improves the neighborhood search methods according to search strategies from near to far,and uses the homogenizing time series to search for the optimal location of the honey source,so as to improve the traversal speed and convergence accuracy of the proposed algorithm.Finally,the proposed algorithm performs experimental simulation on nine standard test functions.It is compared with other improved artificial bee colony algorithms and optimization algorithms from the convergence curve and optimization results,and the six algorithms are reasonably introduced into the logistics distribution problem to find the shortest path.The results show that the proposed optimization algorithm not only strengthens the homogenization of initial honey sources,but also has a more significant optimization effect.It can jump out of the local optimal and find the global optimal solution accurately and quickly.

Key words: Chaos artificial bee colony algorithm, Entropy spectrum analysis, Logistic homogenization, Neighborhood reconstruction, Optimization of honey source distribution

中图分类号: 

  • TP312
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