计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230600082-10.doi: 10.11896/jsjkx.230600082

• 人工智能 • 上一篇    下一篇

基于混沌映射的改进金枪鱼群优化算法对比研究

尹萍, 谈果戈, 宋伟, 谢涛涛, 姜建彪, 宋洪圆   

  1. 浪潮云信息技术股份公司 济南 250101
  • 发布日期:2024-06-06
  • 通讯作者: 谈果戈(tanguoge@inspur.com)
  • 作者简介:(yingping@inspur.com)

Comparative Study on Improved Tuna Swarm Optimization Algorithm Based on Chaotic Mapping

YIN Ping, TAN Guoge, SONG Wei, XIE Taotao, JIANG Jianbiao, SONG Hongyuan   

  1. Inspur Cloud Information Technology Co.,Ltd,Jinan 250101,China
  • Published:2024-06-06
  • About author:YIN Ping,born in 1983,Ph.D.Her main research interests include cloud computing,computer vision,Internet of Things,edge computing and other theories in Industrial Internet,smart cities,digital government and other fields.
    TAN Guoge,born in 1992,Ph.D.His main research interests include swarm intelligence optimization algorithm,cloud computing resource scheduling algorithm and virtualization.

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摘要： Kubernetes作为当前云资源管理的标准平台,因其默认调度机制的局限性,目前普遍采用基于群智能优化算法的改进方法进行Pod的调度。而针对群智能优化算法存在的寻优性能易受初值影响、迭代后期容易早熟收敛等问题,选择金枪鱼群优化(Tuna Swarm Optimization,TSO)作为基础算法,根据混沌映射具有的遍历性、随机性等特点,提出了基于混沌映射的种群初始化优化方案。选择目前研究中普遍涉及的Tent、Logistic等多种混沌映射,分别对金枪鱼种群进行初始化,以提高初始种群的多样性。通过一系列基准测试函数进行仿真实验,对比基于不同混沌映射的改进金枪鱼群优化算法的实验结果,证明了基于混沌映射的优化方案可以有效提高原始TSO算法的收敛速度和寻优精度。

关键词: 金枪鱼群优化算法, 混沌映射, 群智能优化算法, 基准测试函数, Kubernetes

Abstract: As the current standard platform for cloud resource management,Kubernetes generally adopts improved methods based on swarm intelligence optimization algorithms for pod scheduling due to various shortcomings of its default scheduling mechanism.Tuna swarm optimization(TSO) is selected as the basic algorithm in this paper.And according to the ergodicity,randomness and other characteristics of chaos,a chaotic mapping based population initialization scheme is proposed to address the common problems of swarm intelligence optimization algorithms,such as susceptibility to initial values and premature convergence during later iterations.Various chaotic maps,such as Tent,Logistic,and so on,which are commonly involved in current research,are selected to initialize the tuna swarm respectively to improve the diversity of the initial population.Numerical experiments are conducted to compare the experimental results of the improved tuna swarm optimization algorithms based on different chaotic maps.It proves that the population initialization scheme based on chaotic maps can effectively improve the convergence speed and calculation accuracy of the original TSO algorithm.

Key words: Tuna swarm optimization algorithm, Chaotic map, Swarm intelligence optimization algorithm, Benchmark functions, Kubernetes

中图分类号: 

  • TP301.6
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