基于改进灰狼优化算法的WSN覆盖优化

WSN coverage optimization based on improved grey wolf optimization algorithm

针对无线传感器网络(WSN)节点在随机部署时,存在分布不均匀的情况,从而导致覆盖率较低的问题,提出了一种改进的灰狼优化(GWO)算法.首先利用Tent混沌映射初始化种群,增加种群的多样性;其次利用改进的非线性收敛因子,平衡算法的全局搜索能力与局部搜索精度;最后将差分进化(DE)算法的变异、交叉的理念融入GWO算法,避免算法陷入局部最优,并提高算法的收敛速度.基本测试函数仿真结果验证了改进算法的有效性,随后将其应用于WSN覆盖优化问题,可以使节点的分布更加均匀,显著提高覆盖率,进而改善网络性能.

We consider the problem of low coverage of wireless sensor network(WSN)nodes caused by uneven distribution during random deployment.An improved gray wolf optimization(GWO)algorithm was proposed.The population was initialized by using Tent chaotic map to increase the diversity of the population.The improved nonlinear convergence factor was used to balance the global search ability and local search accuracy of the algorithm.Mutation and crossover of differential evolution(DE)algorithm were integrated into GWO algorithm to avoid the algorithm falling into local optimization and improve the convergence speed of the algorithm.The simulation results of the basic test function verify the effectiveness of the improved algorithm.The improved GWO was applied to the WSN coverage optimization problem,which can make the node distribution more uniform,improve the coverage and the network performance.