改进的海洋生物捕食算法在网络成本优化上的应用

高明; 沈艺程; 刘铭

doi:10.11959/j.issn.1000-0801.2025118

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改进的海洋生物捕食算法在网络成本优化上的应用

研究与开发 | 更新时间：2025-09-04

- 改进的海洋生物捕食算法在网络成本优化上的应用
- Application of improved discrete marine predatiors algorithm in network cost optimization
- 电信科学 2025年41卷第8期页码：86-100
- 作者机构：
  
  1.浙江工商大学信息与电子工程学院，浙江杭州 310018
  2.浙江工业大学计算机科学与技术学院，浙江杭州 310023
- 作者简介：
  
  [ "高明（1979- ），男，博士，浙江工商大学副教授，浙江省新型网络标准及应用技术重点实验室副主任，主要研究方向为新型网络体系架构和云计算。" ]
  [ "沈艺程（1998- ），男，浙江工商大学硕士生，主要研究方向为通信网络、云原生、智能优化算法。" ]
  [ "刘铭（1997- ），男，浙江工业大学计算机科学与技术学院博士生，主要研究方向为时空数据分析、网络科学。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61871468);浙江省新型网络标准与应用技术重点实验室项目(2013E10012);浙江省基础公益研究计划项目;The Basic Public Welfare Research Program of Zhejiang Province(LGG20F010015)
- DOI：10.11959/j.issn.1000-0801.2025118
  中图分类号： TP393
- 收稿日期：2024-12-21，
  
  修回日期：2025-03-13，
  
  纸质出版日期：2025-08-20
- 稿件说明：
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高明,沈艺程,刘铭.改进的海洋生物捕食算法在网络成本优化上的应用[J].电信科学,2025,41(08):86-100.

GAO Ming,SHEN Yicheng,LIU Ming.Application of improved discrete marine predatiors algorithm in network cost optimization[J].Telecommunications Science,2025,41(08):86-100.
高明,沈艺程,刘铭.改进的海洋生物捕食算法在网络成本优化上的应用[J].电信科学,2025,41(08):86-100. DOI： 10.11959/j.issn.1000-0801.2025118.

GAO Ming,SHEN Yicheng,LIU Ming.Application of improved discrete marine predatiors algorithm in network cost optimization[J].Telecommunications Science,2025,41(08):86-100. DOI： 10.11959/j.issn.1000-0801.2025118.

摘要

容器技术作为轻量级虚拟化方案，已成为多云网络架构的核心支撑技术，但跨云通信的高昂成本仍是关键挑战。创新点在于将多云网络资源调度问题建模为二次分配问题（QAP），并提出一种改进的离散海洋捕食算法（DMPA）。DMPA通过以下策略显著提升性能：基于均匀分布与伪反向学习的混合种群初始化策略，避免初始解陷入局部最优；引入自适应收敛因子动态调整搜索范围，平衡全局探索与局部开发；采用可变交换间隔的2-exchange突变策略，增强种群多样性；结合禁忌搜索优化精英解，避免重复搜索。实验表明，DMPA在30个QAP实例中22个达到已知最优解，平均偏差率低于3%；在多云网络真实数据集下，优化效果明显，通信成本有所降低。相比MPA、HPSO等算法，DMPA在优化精度与稳定性上均表现突出，为多云网络成本优化提供了高效解决方案。

Abstract

Container technology

as a lightweight virtualization solution

remains a core supporting technology for multi-cloud network architectures. However

the high cost of cross-cloud communication remains a critical challenge. The key innovation of models of the resource scheduling problem in multi-cloud networks as a quadratic assignment problem (QAP) and an improved discrete marine predators algorithm (DMPA) was proposed. The DMPA was significantly enhanced performance through the following strategies: a hybrid population initialization strategy based on uniform distribution and pseudo-reverse learning

which avoided the initial solution from falling into local optima; an adaptive convergence factor was introduced to dynamically adjust the search range

balancing global exploration and local exploitation; a 2-exchange mutation strategy with variable exchange intervals was employed to enhance population diversity; the integration of tabu search to optimize elite solutions and avoid repetitive searches. Experiments show that DMPA achieves known optimal solutions in 22 out of 30 QAP instances

with an average deviation rate of less than 3%. Under real-world multi-cloud network datasets

the optimization effect is significant

and communication costs are reduced. Compared to algorithms such as MPA and HPSO

DMPA demonstrates outstanding performance in both optimization accuracy and stability

providing an efficient solution for cost optimization in multi-cloud networks.

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references

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