改进麻雀搜索算法在WSNs覆盖优化中的应用

Application of improved SSA in WSNs coverage optimization

针对无线传感器网络(WSNs)中随机部署传感器节点而造成节点覆盖率低的问题,提出基于改进麻雀搜索算法(SSA)的覆盖优化方法。首先,利用反向学习策略初始化种群,使种群具有多样性;其次,利用鲸鱼算法中的随机搜索策略改进麻雀算法中发现者的更新方式,提高麻雀种群的全局搜索能力;最后,利用萤火虫算法对麻雀个体进行扰动更新,避免算法陷入局部最优。通过实验结果分析,改进后的算法在优化过程中提升了收敛速度和网络覆盖率,从而改善了网络的整体性能。

Aiming at the problem of low coverage rate of node caused by random deployment of sensor nodes in wireless sensor networks(WSNs),a coverage optimization method based on improved sparrow search algorithm(SSA)is proposed.Firstly, the inverse learning strategy is used to initialize the population, so that make the population has diversity.Secondly, the random search strategy of whale algorithm is used to improve the update method of the finder in sparrow algorithm, and the global search ability of sparrow population is improved.Finally, the firefly algorithm is used to perturb sparrow individuals to avoid the algorithm falling into local optimum.Through the analysis of experimental results, the improved algorithm improves the network coverage rate and convergence speed in the process of optimization, so as to improve the overall performance of the networks.