基于CPSO的无线传感器网络路由优化

研究无线传感器网络路由优化问题,由于无线传感器节点的能量受到限制,通信过程能量损耗,影响网络的性能。传统粒子群算法难以获得最优网络路由方案。为延长网络生存时间,结合粒子群的快速性和混沌的遍历性优点,提出了一种混沌粒子群(CPSO)的无线网络路由优化方法。通过粒子群算法的自组织、动态寻优能力,并通过混沌机制对粒子群进行混沌扰动,增加多样性,加快最优路由优化速度,使网络最优路由和能量消耗间尽量平衡。仿真结果表明,相对于传统优化算法,CPSO提高了无线传感器网络路由优化速度,减少网络能量消耗,有效延长了网络生存时间,为提高整个网络通信效率提供了参考。

Application of Chaotic Particle Swarm Optimization in Wireless Sensor Network Path Optimization

This paper put forward a wireless sensor network(WSN) routing optimization method based on Chaos particle swarm optimization algorithm(CPSO) by combining the advantage of fast and chaotic particle swarm.Initial particle swarms were produced by chaos variable,the offspring were disturbed,and the magnitude of the perturbation was adjusted gradually in the optimization process,which overcame the problem that PSO is easy to fall into local optimization defect.CPSO was used for solving WSN routing optimization problem,the corresponding mathematical model was established,and the corresponding optimization algorithm was designed.The simulation results show that,compared with the genetic algorithm and particle swarm optimization algorithm,the CPSO can improve the convergence speed and search ability,quickly find the optimal path in WSN,and improve the efficiency of network communication.