基于混沌映射的改进金枪鱼群优化算法对比研究
TP301.6; 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...
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| Published in: | 计算机科学 Vol. 51; no. z1; pp. 261 - 270 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | Chinese |
| Published: |
浪潮云信息技术股份公司 济南 250101
16.06.2024
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| Subjects: | |
| ISSN: | 1002-137X |
| Online Access: | Get full text |
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| Abstract | TP301.6; 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. |
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| AbstractList | TP301.6; 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. |
| Abstract_FL | 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,ran-domness 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 conver-gence during later iterations.Various chaotic maps,such as Tent,Logistic,and so on,which are commonly involved in current re-search,are selected to initialize the tuna swarm respectively to improve the diversity of the initial population.Numerical experi-ments 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. |
| Author | 姜建彪 谢涛涛 宋洪圆 谈果戈 尹萍 宋伟 |
| AuthorAffiliation | 浪潮云信息技术股份公司 济南 250101 |
| AuthorAffiliation_xml | – name: 浪潮云信息技术股份公司 济南 250101 |
| Author_FL | SONG Wei JIANG Jianbiao TAN Guoge XIE Taotao SONG Hongyuan YIN Ping |
| Author_FL_xml | – sequence: 1 fullname: YIN Ping – sequence: 2 fullname: TAN Guoge – sequence: 3 fullname: SONG Wei – sequence: 4 fullname: XIE Taotao – sequence: 5 fullname: JIANG Jianbiao – sequence: 6 fullname: SONG Hongyuan |
| Author_xml | – sequence: 1 fullname: 尹萍 – sequence: 2 fullname: 谈果戈 – sequence: 3 fullname: 宋伟 – sequence: 4 fullname: 谢涛涛 – sequence: 5 fullname: 姜建彪 – sequence: 6 fullname: 宋洪圆 |
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| ClassificationCodes | TP301.6 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Comparative Study on Improved Tuna Swarm Optimization Algorithm Based on Chaotic Mapping |
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| Keywords | Benchmark functions 金枪鱼群优化算法 Chaotic map Swarm intelligence optimization algorithm 群智能优化算法 混沌映射 Tuna swarm optimization algorithm 基准测试函数 Kubernetes |
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| Title | 基于混沌映射的改进金枪鱼群优化算法对比研究 |
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