Enhanced zebra optimization algorithm and applications of power allocation in SDWN

LI Jiajia; DONG Ligang; JIANG Xian

doi:10.11959/j.issn.1000-0801.2025017

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Enhanced zebra optimization algorithm and applications of power allocation in SDWN

Research and Development | 更新时间：2025-02-07

- Enhanced zebra optimization algorithm and applications of power allocation in SDWN
- Telecommunications Science Vol. 41, Issue 1, Pages: 148-163(2025)
- 作者机构：
  
  浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院），浙江杭州 310018
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62301488);The Key Technologies Research & Development Program of Zhejiang Province(2021C01036)
- DOI：10.11959/j.issn.1000-0801.2025017
  CLC： TN929.5
- Received：01 November 2024，
  
  Revised：2024-12-24，
  
  Published：20 January 2025
- 稿件说明：
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李佳佳,董黎刚,蒋献.增强型斑马优化算法及其在SDWN功率分配中的应用[J].电信科学,2025,41(01):148-163.

LI Jiajia,DONG Ligang,JIANG Xian.Enhanced zebra optimization algorithm and applications of power allocation in SDWN[J].Telecommunications Science,2025,41(01):148-163.
李佳佳,董黎刚,蒋献.增强型斑马优化算法及其在SDWN功率分配中的应用[J].电信科学,2025,41(01):148-163. DOI： 10.11959/j.issn.1000-0801.2025017.

LI Jiajia,DONG Ligang,JIANG Xian.Enhanced zebra optimization algorithm and applications of power allocation in SDWN[J].Telecommunications Science,2025,41(01):148-163. DOI： 10.11959/j.issn.1000-0801.2025017.

摘要

为解决多用户通信中共享软件定义无线网络（software defined wireless network，SDWN）资源导致的互扰问题，提出一种增强型斑马优化算法（enhanced zebra optimization algorithm，EZOA），实现保密率最大化的功率分配。在斑马优化算法中引入探索因子和扰动参数，增强其全局搜索和局部开发能力，并改进觅食策略以提升算法多样性。实验结果表明，相较于其他6种成熟算法，EZOA在IEEE CEC 2014基准函数集上，表现出优异的竞争力，并在SDWN功率分配问题中，与其他5种算法相比，EZOA的用户保密率平均提高5.46%，且收敛速度最快。

Abstract

To address the issue of interference caused by shared resources in multi-user communications in software defined wireless network (SDWN)

an enhanced zebra optimization algorithm (EZOA) was proposed to achieve power allocation that maximizes the secrecy rate. By introducing exploration factors and perturbation parameters into the zebra optimization algorithm

EZOA enhanced global search and local exploitation capabilities

while also improving the foraging strategy to increase algorithm diversity. Experimental results demonstrate that EZOA exhibits superior competitiveness compared to six other well-established algorithms on the IEEE CEC 2014 benchmark function set. In the SDWN power allocation problem

EZOA achieves an average improvement of 5.46% in user secrecy rate and the fastest convergence speed compared to five other algorithms.

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Keywords

references

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