基于粒子间距调整改进PSO算法Improved Particle Swarm Optimization Based on Adjusting Particle Spacing
冯颖,高文华,康琳
摘要(Abstract):
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。
关键词(KeyWords): 无线传感器网络;覆盖率;粒子群算法;粒子间距
基金项目(Foundation): 山西省青年科技研究基金(20171D221109);; 山西省重点研发计划(201903D321012)
作者(Author): 冯颖,高文华,康琳
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