WSN中基于改进麻雀搜索算法的多目标覆盖优化

Multi-objective coverage optimization of WSN based on improved sparrow search algorithm

针对无线传感器网络中(WSNs)节点随机部署造成覆盖不充分问题,提出了一种改进麻雀搜索-覆盖率增量(ISSA-ICR)算法。首先,ISSA针对探索者向原点收敛的问题,改进探索者位置更新方式,避免算法陷入局部最优解;其次,在算法迭代阶段引入以迭代次数为自由度参数的t分布扰动以及探索者-追随者数量动态调整策略,平衡算法全局和局部搜索能力;再次,采用随机回归的越界处理策略,合理处理个体越界重定位问题,并确定节点待部署位置;最后,基于ICR策略构建节点调度优化模型以确定最终部署位置。仿真结果表明,与麻雀搜索算法、标准粒子群优化算法及自适应虚拟力扰动麻雀搜索算法相比,ISSA-ICR节点覆盖多目标优化算法分别提升了目标区域4.96%、8.81%及3.84%的覆盖率,使节点分布更均匀,同时减少了节点移动距离。

The randomly deployed nodes will lead to the insufficient coverage in wireless sensor networks(WSNs). To solve this problem, an improved sparrow search algorithm-increment of coverage ratio(ISSA-ICR) was proposed. Firstly, to solve the problem that the producer converging to the origin, ISSA modified the location update method of the producer to avoid the algorithm falling into the local optimal solution;Secondly, to balance the global and local search ability of the algorithm, t-distribution disturbance with the number of iterations as the degree of freedom parameter and the dynamic adjustment strategy of the number of producers-scroungers were proposed;Thirdly, random regression cross-border processing strategy was adopted to solve the problem of individual cross-border relocation, and the candidate location of nodes to be deployed was determined;Finally, the node scheduling optimization model was constructed based on ICR strategy to determine the final deployment location. The simulation results show that compared with sparrow search algorithm, standard particle swarm optimization and adaptive virtual force disturbance sparrow search algorithm, ISSA-ICR can respectively improve 4.96%, 8.81% and 3.84% coverage ratio compared with the three algorithms, meanwhile reducing the nodes moving distance.