基于改进麻雀算法的无线传感器网络覆盖优化研究

Coverage optimization of wireless sensor networks based on lengthen sparrow algorithm

针对基本麻雀搜索算法在无线传感器网络覆盖问题中收敛速度较慢、容易陷入局部最优等问题,提出了一个基于改进麻雀算法的优化方案。首先,利用Sobol序列和无限次折叠的ICMIC混沌映射对种群进行初始化,增加了种群遍历性和多样性,为算法的全局寻优奠定了基础。其次,引入混沌映射因子的正余弦算法策略,增强了探索者探索未知区域的能力,提高了算法的全局搜索性能。再次,利用混合变异策略加快算法收敛速度,并改善算法跳出局部最优的能力。最后,将提出的改进麻雀算法应用到无线传感器网络覆盖优化问题中进行仿真实验。仿真结果表明,提出的改进算法相比基本麻雀算法将网络节点的覆盖率提高了7%,同时增强了网络的整体性能,并具有实用性、稳定性和鲁棒性。

The paper addresses the problem that the sparrow search algorithm(SSA)converges slowly and easily falls into local optimum when it is applied to wireless sensor network coverage optimization.To solve this problem,a lengthen SSA(LSSA)is developed based on the chaotic mapping factor and positive cosine algorithm strategy,and the global variation method.To be specific,the population is initialized based on Sobol sequence and ICMIC chaotic mapping with infinite folding,which can increase the population diversity.Moreover,the positive cosine algorithm strategy with chaotic mapping factor is introduced to enhance the ability of exploring the unknown region,improving the global search performance.Then the hybrid variation strategy is used in order to accelerate the convergence speed and improve the algorithm's ability to jump out of the local optimum.The simulation results show that the improved algorithm increases the coverage of network nodes by 7%,enhances the overall performance of the network,and has practicality,stability,and robustness.