基于改进人工鱼群算法在无线传感网络覆盖优化中的研究

Improvement Artificial Fish-Swarm in Wireless Sensor Network Coverage Optimization

针对无线传感网中的节点存在冗余以及网络成本增加等问题,本文提出了一种改进的人工鱼群算法的覆盖优化.本文首先建立以节点的利用率和覆盖率的数学模型,其次对人工鱼群算法进行改进,一是在初始化阶段使用概率密度函数来对鱼群个体的初始位置进行分布,有效的避免鱼群个体初始无序的状态;二是在觅食阶段中使用混沌算法对鱼群位置个体进行干扰,有效的减少鱼群个体向局部最优解的靠近的时间;三是在聚群行为中使用高斯变异,从而减少全局最优解的产生的时间.改进后的人工鱼群算法对模型求解,得到最优的覆盖方案,仿真实验表明能够有效的提高网络覆盖效果,以及节点的利用率,降低网络成本消耗.

Aiming at redundant nodes in wireless sensor networks and increased network costs, an improved coverage optimization method artificial fish swarm algorithm is proposed in this paper. First of all, mathematical model of nodes＇ utilization rate and coverage rate is established, and then artificial fish swarm algorithm is improved One, probability density function is used in the initial stage for the distribution of the initial location of individuals in the fish swarm so as to effectively avoid chaotic state of the fish swarm individual in the beginning; two, chaos algorithm is used in the foraging stage to interfere with the location of fish swarm individuals so as to effectively reduce the time of fish swarm individual getting close to the optimal solution; three, Gaussian mutation is used in the cluster behaviors so as to reduce the time of producing the optimal solutions. The improved artificial fish swarm can get the optimal coverage scheme while solving the model. Simulation experiments show that it can effectively improve the effect of network coverage as well as node＇s utilization rate, and reduce the network costs.