改进人工蜂群算法的无线传感器网络覆盖优化

Wireless sensor networks coverage optimization based on improved artificial bee colony algorithm

由于无线传感器网络覆盖属于组合优化范畴,为了在有限节点数量情况下提高其覆盖率,引入人工蜂群算法并进行改进。通过注入全局最优个体反馈来提高收敛速度,并升级了侦查蜂更新方法,采用一维高斯变异的方法,充分利用粒子先验知识,使粒子既能够保证足够的活力,又提高了算法的全局搜索能力。在40个节点数量条件下进行仿真实验,提出的改进算法覆盖率达到了87.2%,与改进蛙跳算法和标准人工蜂群算法的覆盖率相比分别提高了1.6%和3.87%。

Since the wireless sensor networks coverage belongs to the combinatorial optimization category,the artificial bee colony algorithm is introduced and perfected to improve the coverage rate in the condition of limited node quantity.The global optimal individual feedback is injected to improve the convergence rate,and update the refresh method of investigation bee.The one-dimensional Gaussian mutation method and particle prior knowledge are used fully to make the particles active,and improve the global search capability of the algorithm.The algorithm with 40 nodes was carried out with simulation experiment.The experimental results show that the coverage rate of the improved algorithm can reach up to 87.2%,and is increased by 1.6% and3.87% respectively than that of the improved shuffled frog leaping algorithm and standard artificial bee colony algorithm.