基于萤火虫群优化算法的无线传感器网络覆盖优化

Coverage Optimization in Wireless Sensor Networks Based on Glowworm Swarm Optimization

无线传感器网络加速了无线通信的发展,无线网络覆盖率的高低可直接影响网络的性能。为改善传感器节点随机分布时的不合理部署问题以提高网络覆盖率,提出一种相对较优的无线传感器网络覆盖算法。针对粒子群优化(particle swarm optimization,PSO)算法局部搜索能力存在不足、容易陷入局部极值点、无法得到最优结果的问题,引入局部搜索能力较强的萤火虫群优化(glowworm swarm optimization,GSO)算法,实现网络有效覆盖率的提高,对节点实现快速覆盖。最后通过实验验证,结果表明,提出的改进GSO(improved GSO,IGSO)算法相较于传统鲸鱼优化算法(whale optimization algorithm,WOA)、PSO算法在网络覆盖率上有较大提升。

Wireless sensor network accelerates the development of wireless communication,and the level of wireless network coverage will directly affect the performance of networks.Therefore,to improve the unreasonable deployment of sensor nodes due to random distribution,a relatively optimal coverage algorithm for wireless sensor networks was presented.The local search ability of the particle swarm algorithm was insufficient,easy to fall into the local extreme points,and could not get the optimal results.In this paper,the problem of the particle swarm algorithm was compensated by introducing the glowworm swarm optimization(GSO)algorithm with strong local search ability,which could improve the effective coverage of the network and complete the fast coverage of nodes.Finally,the experimental validation showed that the improved GSO(IGSO)algorithm proposed had better network coverage than the traditional whale optimization algorithm(WOA)and particle swarm optimization(PSO)algorithm.