基于改进蝙蝠算法的WSNs覆盖优化策略

Coverage optimization strategy of WSNs based on improved bat algorithm

对有效提高无线传感器网络覆盖率的问题进行研究,提出了一种自适应虚拟力导向蝙蝠算法(Adaptive Virtual Force-guided Bat Algorithm,AVFBA)的无线传感器网络(Wireless Sensors Networks,WSNs)覆盖优化策略。该策略引入指数衰减函数,通过自适应调整虚拟力算法迭代步长,加快算法收敛速度。将莱维飞行融入到蝙蝠算法中,扰动最优蝙蝠的位置,从而提高算法跳出局部最优的能力。为了验证所提策略的有效性,将其应用在WSNs覆盖优化中,仿真结果表明,所提策略拥有更高的覆盖率和覆盖效率,能有效降低节点冗余,使节点分布更加均匀。

In order to effectively improve the coverage of wireless sensor networks(WSNs),a WSNs coverage optimization algorithm based on the adaptive virtual force-guided bat algorithm(AVFBA)is proposed.The exponential attenuation function is introduced,and the iteration steps of the virtual force algorithm are adaptively adjusted to speed up the convergence performance of the algorithm.Then,the Levy flight is integrated into the bat algorithm,which disturbs the optimal bat position and improves the algorithm's ability to jump out of the local optimum.The algorithm is applied to WSNs to improve coverage optimization,with the results illustrating that the algorithm has higher coverage rate and efficiency,and can effectively reduce the redundancy of nodes,which helps the nodes to distribute more evenly.