Multi-Objective White Shark Optimizer for Edge-Side Industrial Microservice Deployment

LI Xiaobin; GOU Kunyao; YIN Chao

doi:10.7652/xjtuxb202508001

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Multi-Objective White Shark Optimizer for Edge-Side Industrial Microservice Deployment

Special Topic　Industrial Internet and Manufacturing Services | 更新时间：2025-07-30

- Multi-Objective White Shark Optimizer for Edge-Side Industrial Microservice Deployment
- Journal of Xi’an Jiaotong University Vol. 59, Issue 8, Pages: 1-10(2025)
- 作者机构：
  
  重庆大学绿色智能制造研究所， 400044，重庆
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202508001
  CLC： TP277
- Received：06 January 2025，
  
  Online First：15 April 2025，
  
  Published：10 August 2025
- 稿件说明：
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LI Xiaobin, GOU Kunyao, YIN Chao. Multi-Objective White Shark Optimizer for Edge-Side Industrial Microservice Deployment[J]. Journal of Xi’an Jiaotong University, 2025, 59(8): 1-10.
DOI：

LI Xiaobin, GOU Kunyao, YIN Chao. Multi-Objective White Shark Optimizer for Edge-Side Industrial Microservice Deployment[J]. Journal of Xi’an Jiaotong University, 2025, 59(8): 1-10. DOI： 10.7652/xjtuxb202508001.

摘要

针对在有限资源的边缘节点上部署工业微服务时难以兼顾节点的资源利用和负载均衡状况的问题，提出了一种改进型多目标白鲨优化算法。考虑工业微服务部署时边缘节点资源利用和负载状况的协同优化问题，建立了一个以边缘节点计算资源余量、服务通信能耗、负载均衡状况和存储资源余量为指标的多目标优化模型；针对该模型的求解，从初始种群质量、收敛速度、跳出局部最优能力以及优质解的保留4个方面，分别引入了混沌映射初始化、自适应权重因子、差分进化算子以及精英保留策略对多目标白鲨优化算法进行改进。模型求解实验结果表明，相较于原始多目标白鲨优化算法，改进型多目标白鲨优化算法在上述4个指标上分别优化了13.8%、37.1%、63.9%、47.4%，相较于二代遗传算法则分别优化了63.2%、53.2%、39.1%、63.6%，且所提算法收敛速度更快。

Abstract

To address the challenge of balancing resource utilization and load balancing in edge nodes with limited resources during industrial microservice deployment

an improved multi-objective white shark optimizer (IMOWSO) is proposed. Considering the co-optimization of resource utilization and load conditions in edge nodes during industrial microservice deployment

a multi-objective optimization model is established with four key metrics: computational resource margin of edge nodes

service communication energy consumption

load balancing status

and storage resource margin. To solve this model

the improved algorithm enhances the original multi-objective white shark optimizer (MOWSO) in four aspects: initial population quality

convergence speed

ability to escape local optima

and preservation of high-quality solutions. Specifically

chaotic mapping initialization

adaptive weight factors

differential evolution operators

and an elite retention strategy are introduced. Experimental results demonstrate that compared to the original MOWSO

the proposed IMOWSO achieves optimizations of 13.8%

37.1%

63.9%

and 47.4% in the four metrics

respectively. Furthermore

when compared to the second-generation genetic algorithm

the improvements reach 63.2%

53.2%

39.1%

and 63.6%

respectively

while also exhibiting faster convergence speed.

关键词

Keywords

references

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