摘要
针对异构无线传感器网络节点高密度部署和事件发生存在"热点区域"问题,以区域覆盖率最大和网络能耗最小为优化目标,提出了一种基于多目标优化的二进制粒子群算法,对节点部署进行多目标优化。该算法采用概率感知模型,引入强支配系数使得解分布均匀,结合Pareto最优解选择排序和基于自适应权重的适应度分配,进而获得异构节点部署解。仿真结果表明:该算法能对目标空间进行广泛搜索,与NSGA—Ⅱ算法相比,算法具有良好的收敛性,能有效地提高网络的覆盖率和降低网络能耗。
An algorithm based on multi-objective optimization binary particle swarm is proposed to solve the problem of node high-density deployment and events existing ＂hot spots＂ in heterogeneous wireless sensor networks. Area coverage is maximum and energy consumption is minimum are the optimization goals. Pro]~ability sensing model is used and strong predominance coefficient is introduced to provide a good diversity, both pareto optimum solution sorting and adaptive weight fitness assignment methods are used in this algorithm to get heterogeneous wireless sensor network＇ s node deployment solutions. Compared with NSGA-Ⅱ , the algorithm has good astringency and effectively improve network coverage and reduce energy consumption.
出处
《传感器与微系统》
CSCD
北大核心
2012年第3期29-31,35,共4页
Transducer and Microsystem Technologies
基金
国家科技重大专项基金资助项目(200912X07528-003-09)
重庆市科技攻关计划资助项目(CSCT
2010AA2036)
关键词
异构无线传感器网络
节点部署
多目标优化
粒子群算法
heterogeneous wireless sensor networks node deployment
multi-objective optimization particleswarm algorithm
