基于粒子间距调整改进PSO算法  

Improved Particle Swarm Optimization Based on Adjusting Particle Spacing

作　　者：冯颖 高文华[1] 康琳[1] FENG Ying;GAO Wen-hua;KANG Lin(School of Electronics and Information Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学电子信息工程学院,太原030024

出　　处：《太原科技大学学报》2021年第6期463-468,474,共7页Journal of Taiyuan University of Science and Technology

基　　金：山西省青年科技研究基金(20171D221109);山西省重点研发计划(201903D321012)。

摘　　要：基于无线传感器感知模型,提出了一种粒子间距调整改进粒子群优化算法(Improved particle swarm optimization based on adjusting particle spacing,APS-PSO),利用APS-PSO算法优化WSN在目标区域内的部署,有效提高了WSN的覆盖率。首先,针对粒子越界和粒子间发生重叠等多样性消失的问题,在迭代过程中引入粒子间距调整(APS)来增强粒子多样性,其次,通过为种群中粒子细化寻优方向,能够尽可能的重启早熟粒子。通过目标区域离散化以及传感器节点的特性定义目标函数,将其代入到APS-PSO中,从而找到较好的覆盖方案。通过Matlab仿真结果表明:该算法提高了传感器节点分布的均匀度,网络的覆盖率也得到了提高,而且也具有相对较好的稳定性。In this paper, improved particle swarm optimization based on particle spacing( APS-PSO)was proposed to optimize the deployment of WSN in the target region and effectively improve the coverage of WSN.Firstly, particle spacing adjustment(APS)was introduced in the iteration process to enhance the diversity of particles for the problem of the disappearance of diversity such as the overstepping and overlapping of particles.Secondly, the particles could jump out of the local optimum as much as possible by adding exploration directions to the particles in PSO.The objective function is defined by the discretization of the target region and the characteristics of sensor nodes, and substituted into APS-PSO to find a better coverage scheme.The results of matlab simulation show that the algorithm improves the uniformity of sensor node distribution and network coverage, and has relatively good stability.

关 键 词：无线传感器网络 覆盖率 粒子群算法 粒子间距 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]