多策略灰狼算法在WSN上的覆盖优化研究

Research on coverage optimization of multi strategy grey wolf algorithm on WSN

针对随机部署无线传感网络出现的覆盖率低的问题,提出了一种多策略灰狼(MSGWO)算法的无线传感网络覆盖优化方法。首先,为平衡全局与局部搜索,提出双曲正切的非线性收敛因子;其次,采用差分变异重构包围步长来降低算法陷入局部最优的概率;然后,为加快算法的收敛速度与精度,利用瞬态搜索优化方程更新灰狼位置;接着融合莱维飞行策略增加空间搜索多样性;最后,引入边界越位策略避免灰狼个体越界重定位问题。仿真结果表明,MSGWO算法相比SSA、LGWO、PSO和PSOGWO算法的平均覆盖率增量分别为12.52%、6.054%、7.53%和3.45%,该算法具有较高的平均覆盖率和更优的节点分布状态。

Aiming at the problem of low coverage of wireless sensor networks deployed randomly,a Multi Strategy gray wolf(MSGWO) algorithm for wireless sensor network coverage optimization is proposed.Firstly,in order to balance the global and local search,a nonlinear convergence factor of hyperbolic tangent is proposed;Secondly,the bounding step size is reconstructed by differential mutation to reduce the probability of the algorithm falling into local optimization;Then,in order to speed up the convergence speed and accuracy of the algorithm,the gray wolf position is updated by using the transient search optimization equation;Then,Levy flight strategy is integrated to increase the diversity of space search;Finally,the boundary offside strategy is introduced to avoid the relocation of gray wolf individuals.The simulation results show that compared with SSA,LGWO,PSO and PSOGWO,the average coverage increment of MSGWO algorithm is 12.52%,6.054%,7.53% and 3.45% respectively.This algorithm has higher average coverage and better node distribution.