改进麻雀搜索算法下的WSN节点覆盖优化

WSN Node Coverage Optimization Based on Improved Sparrow Search Algorithm

摘要: 针对麻雀搜索算法(SSA)存在的种群多样性不足，容易陷入局部最优解等问题，提出一种基于自适应t分布变异的改进麻雀搜索算法(ISSA)。通过在基本麻雀算法中加入t分布型随机扰动项，将算法的迭代次数作为t分布的自由度参数，增加了算法的种群多样性，均衡了算法的全局探寻和局部开发能力。采用4种基准函数评估ISSA的性能，并将ISSA算法应用在无线传感器网络(WSN)节点的覆盖优化问题。仿真实验表明，改进的麻雀搜索算法在收敛精度和速度上有显著提升，网络覆盖率相较于对照算法有一定的提高。

Abstract: Aiming at the problems of sparrow search algorithm (SSA), such as population diversity deficiency and being easy to fall into local optimal solution, an improved sparrow search algorithm (ISSA) based on adaptive t-distribution mutation is proposed. By adding the random disturbance term of t-distribution into the basic sparrow algorithm, the iteration times of the algorithm are taken as the degree of freedom parameter of t-distribution, which increases the population diversity of the algorithm and balances the global searching and local development ability of the algorithm. Four benchmark functions are used to evaluate the performance of ISSA, and the ISSA is applied to the coverage optimization of wireless sensor network (WSN) nodes. The simulation results show that the improved sparrow search algorithm has a remarkable improvement in the convergence accuracy and speed, and the network coverage has a certain improvement compared with the comparison algorithms.