Clustering routing algorithm for wireless sensor networks based on chaotic particle swarm optimization

LI Hui; LIAO Changrong; LIAO Xiaojuan; LIU Lei

Chinese

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Clustering routing algorithm for wireless sensor networks based on chaotic particle swarm optimization

Calculation & Test | 更新时间：2026-01-22

- Clustering routing algorithm for wireless sensor networks based on chaotic particle swarm optimization
- Vol. 45, Issue 1, Pages: 150-154(2026)
- 作者机构：
  
  1. 重庆科创职业学院人工智能与大数据学院,重庆,402160
  2. 重庆文理学院电子信息工程学院,重庆,402160
  3. 西安电子科技大学通信工程学院,陕西,西安,710126
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393;TP212
- Published：2026
- 稿件说明：
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LI Hui, LIAO Changrong, LIAO Xiaojuan, et al. Clustering routing algorithm for wireless sensor networks based on chaotic particle swarm optimization[J]. 2026, 45(1): 150-154.
DOI：

LI Hui, LIAO Changrong, LIAO Xiaojuan, et al. Clustering routing algorithm for wireless sensor networks based on chaotic particle swarm optimization[J]. 2026, 45(1): 150-154. DOI：

摘要

针对无线传感器网络（WSN）能耗高、传输效率低的问题，旨在探索基于群体智能（SI）的分簇路由算法，以提高WSN的能量效率和传输效率。为此，采用混沌粒子群优化（CPSO）方法，结合WSN特点设计了一种新型分簇算法：首先通过节点分组形成最优簇结构以降低通信开销；其次利用CPSO选择簇头节点负责数据聚合与传输，并管理簇内节点能耗；最后通过全局与局部轮换机制维持节点稳定性。实验结果表明，与传统算法相比，该算法使SI稳定工作时间延长30.10%，网络生命周期提升55.20%，显著降低了能耗并提高了数据传输效率。

Abstract

To address the problems of high energy consumption and low transmission efficiency of wireless sensor network (WSN)

a clustering routing algorithm based on swarm intelligence (SI) is explored to improve the energy efficiency and transmission efficiency of WSN.To this end

a chaotic particle swarm optimization (CPSO) approach is employed

and the characteristics of WSN are incorporated to design a novel clustering algorithm.First

nodes are grouped to form an optimal cluster structure to reduce communication overhead.Second

CPSO is used to select cluster head nodes responsible for data aggregation and transmission

and the energy consumption of nodes within the cluster is managed by the cluster head nodes.Finally

global and local rotation mechanisms are used to maintain node stability.Experimental results show that

compared with traditional algorithms

this algorithm extends the SI stable operation time by 30.10% and the network lifetime by 55.20%

significantly reducing energy consumption and improving data transmission efficiency.

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