WSNs覆盖的拟物力导向粒子群优化策略

Coverage Strategy of Virtual Material Force-directed Particle Swarm Optimization in Wireless Sensor Networks

针对无线传感器网络的重复覆盖和算法耗时问题,提出一种拟物力导向的粒子群覆盖优化策略。通过仿真实验对该策略进行优化性能测试,与粒子群算法、粒子进化的多粒子群算法、传统遗传算法和新量子遗传算法的优化效果相比,该策略覆盖率分别提高9.5%、1.7%、6.03%和3.71%,收敛速度分别提高23.2%、1.8%、24.5%和24.5%。结果表明该优化策略具有比上述4种算法更好的覆盖优化效果。

Aiming at the problem of repeat coverage and algorithm taking too much time,this paper proposes a coverage optimization strategy of Virtual Material Force-directed Particle Swarm Optimization（VMFPSO） in Wireless Sensor Networks（WSNs）.The strategy undergoing optimization performance test is analyzed through the simulation experiment.Coverage rate increases by 9.5 percent,1.7 percent,6.03 percent and 3.71 percent and convergence rate increases 23.2 percent,1.8 percent,24.5 percent and 24.5 percent compared with elementary PSO,the evolution of Multi-particle Particle Swarm Optimization（MPSO）,the traditional genetic algorithms（CGA） and quantum of the New Genetic Algorithm（NQGA） about the optimization effectiveness.Results show that the VMFPSO strategy has better coverage optimization effectiveness than PSO,MPSO,CGA,NQGA.