基于特征点集GABC算法的WSN覆盖优化

针对人工蜂群算法利用网格点计算网络覆盖率会导致计算量大且容易陷入局部最优解的问题，提出一种基于特征点集的全局最优解人工蜂群算法优化无线传感器网络。首先将目标区域划分成有限个特征点，用传感器对特征点的覆盖来转化为对若干特征点的覆盖计算，减少求解覆盖率的计算量，进而描述整个网络的覆盖情况。然后在特征点集的基础上，将全局最优解人工蜂群算法成功应用在网络覆盖领域，并且重点对比标准人工蜂群算法和基于全局最优解人工蜂群算法在网络覆盖上的性能。仿真实验结果表明基于全局最优解人工蜂群算法优化节点覆盖后，覆盖率得到有效的提升且不易陷入局部最优解。

WSN Coverage Optimization Based on GABC of Feature Point Set

In order to solve the problem that the artificial bee colony algorithm uses grid points to calculate the network coverage, which will lead to a large amount of calculation and is easy to fall into the local optimal solution, a global optimal solution based on feature points set is proposed to optimize wireless sensor networks. Firstly, the target area is divided into a limited number of feature points, and the coverage of the sensor is transformed into the coverage calculation of several feature points, which reduces the calculation of coverage rate and describes the coverage of the whole network. Then, on the basis of feature point set, the global optimal solution artificial bee colony algorithm is successfully applied in the field of network coverage, and the performance of standard artificial bee colony algorithm and artificial bee colony algorithm based on global optimal solution in network coverage is compared. Simulation experiment results show that after optimizing node coverage based on the global optimal solution artificial bee colony algorithm, the coverage rate is effectively improved and it is not easy to fall into the local optimal solution.