自适应混沌量子粒子群算法及其在WSN覆盖优化中的应用

doi:10.11772/j.issn.1001-9081.2017092372

计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 1064-1071.DOI: 10.11772/j.issn.1001-9081.2017092372

自适应混沌量子粒子群算法及其在WSN覆盖优化中的应用

周海鹏^1,2, 高芹^1,2, 蒋丰千^1,2, 余大为^1,2, 乔焰^1,2, 李旸^1,2

1. 安徽农业大学信息与计算机学院, 合肥 230036;
2. 农业部农业物联网技术集成与应用重点实验室(安徽农业大学), 合肥 230036

收稿日期:2017-10-09 修回日期:2017-12-05 出版日期:2018-04-10 发布日期:2018-04-09
通讯作者: 李旸
作者简介:周海鹏(1990-),男,安徽宣城人,硕士研究生,主要研究方向:计算机网络通信、智能计算;高芹(1987-),女,安徽滁州人,硕士研究生,主要研究方向:计算机软件、智能农业信息化;蒋丰千(1994-),男,安徽滁州人,硕士研究生,主要研究方向:农业物联网、智能农业信息化;余大为(1994-),男,安徽安庆人,硕士研究生,主要研究方向:软件定义网络;乔焰(1984-),女,山东聊城人,副教授,博士,CCF会员,主要研究方向:网络故障诊断与性能测量;李旸(1963-),男,安徽怀远人,教授,博士,主要研究方向:计算机网络分析管理、智能交通、智能建筑与安全防范、农业网络信息。
基金资助:
国家自然科学基金资助项目（61402013）。

Application of self-adaptive chaotic quantum particle swarm algorithm in coverage optimization of wireless sensor network

ZHOU Haipeng^1,2, GAO Qin^1,2, JIANG Fengqian^1,2, YU Dawei^1,2, QIAO Yan^1,2, LI Yang^1,2

1. School of Information and Computer Science, Anhui Agriculture University, Hefei Anhui 230036, China;
2. Key Laboratory of Technology Integration and Application in Agricultural Internet of Things, Ministry of Agriculture(Anhui Agriculture University), Hefei Anhui 230036, China

Received:2017-10-09 Revised:2017-12-05 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61402013).

摘要/Abstract

摘要： 针对传统粒子群优化算法容易陷入局部极值和收敛速度慢等不足，通过研究种群多样性与粒子群算法进化的关系，提出一种动态自适应混沌量子粒子群优化（DACQPSO）算法。该算法将种群分布熵引入粒子群的进化控制，以Sigmoid函数模型为基础，给出了量子粒子群算法收缩扩张系数的计算方法；以平均粒距作为混沌搜索的判别条件进行混沌扰动。将DACQPSO算法应用于无线传感器网络（WSN）的覆盖优化中，并作了仿真分析。实验结果表明，DACQPSO算法在覆盖率指标上比标准粒子群、量子粒子群、混沌量子粒子群算法分别提高了3.3501%、2.6502%和1.9000%，有效地提高了WSN的覆盖性能。

关键词: 无线传感器网络, 网络覆盖率, 种群多样性, 粒子群, 混沌搜索阈值

Abstract: Concerning the problem of traditional Particle Swarm Optimization (PSO) such as slow convergence and being easy falling into local extremum, a Dynamic self-Adaptive Chaotic Quantum-behaved PSO (DACQPSO) was proposed by studying the relationship between population diversity and the evolution of PSO. The population-distribution-entropy was introduced into the evolutionary control of the particle swarm in this algorithm. Based on the Sigmoid function model, the method of calculating the contraction-expansion coefficient of the Quantum-behaved PSO (QPSO) was given. The average-distance-amongst-points was taken as the criterion of chaotic search to carry out a chaotic perturbation. The DACQPSO algorithm was applied to the coverage optimization of Wireless Sensor Network (WSN), and the simulation analysis was carried out. Experimental results show that compared with Standard PSO (SPSO), QPSO and Chaotic Quantum-behaved PSO (CQPSO), the DACQPSO algorithm improves the coverage rate by 3.3501%, 2.6502% and 1.9000% respectively. DACQPSO algorithm improves the coverage performance of WSN, and has better coverage optimization effect than other algorithms.

Key words: Wireless Sensor Network (WSN), network coverage, population diversity, Particle Swarm Optimization (PSO), chaotic search threshold

中图分类号:

周海鹏, 高芹, 蒋丰千, 余大为, 乔焰, 李旸. 自适应混沌量子粒子群算法及其在WSN覆盖优化中的应用[J]. 计算机应用, 2018, 38(4): 1064-1071.

ZHOU Haipeng, GAO Qin, JIANG Fengqian, YU Dawei, QIAO Yan, LI Yang. Application of self-adaptive chaotic quantum particle swarm algorithm in coverage optimization of wireless sensor network[J]. Journal of Computer Applications, 2018, 38(4): 1064-1071.

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自适应混沌量子粒子群算法及其在WSN覆盖优化中的应用

Application of self-adaptive chaotic quantum particle swarm algorithm in coverage optimization of wireless sensor network

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