基于改进萤火虫优化算法的WSN覆盖优化分析

Performance Analysis of Improved Glowworm Swarm Optimization Algorithm and the Application in Coverage Optimization of WSNs

对改进萤火虫算法性能及其在WSNs网络覆盖优化中的应用问题进行了研究。分析了基本萤火虫算法的全局收敛性,针对其收敛效率低的缺陷,给出了算法改进策略,并证明了改进的萤火虫算法以概率1收敛于全局最优解,在此基础上,提出了基于萤火虫优化的网络覆盖算法,建立了以网络均匀度及网络覆盖率为准则的数学模型,推导了节点冗余度与网络覆盖率之间的关系,给出了节点休眠策略,并将节点部署划分成不同的阶段,在每个阶段,分别采用改进的萤火虫算法对模型进行求解,进而得到无线传感器网络最优覆盖,最后对经典测试函数和WSNs网络覆盖问题进行实验仿真,仿真结果表明改进的算法具有更加理想的运算结果,而且能有效地给出WSNs网络覆盖优化方案。

The performance of improved glowworm swarm optimization （GSO）algorithm and its application in coverage optimization of WSNs and the global convergence analysis of basic GSO are analyzed in this paper. In order to improve the GSO convergence efficiency, an improved GSO （IGSO）is presented, which is proved to be guaranteed to the global optimization with probability one. Further, based on IGSO, a new coverage optimization algorithm for WSNs is presented according to the analysis of GSO. A model of coverage optimization in WSNs is built up by taking node uniformity and network coverage rate as the criterion, and the relationship between node redundancy and network coverage rate and the node dormancy strategy are presented. Then the deployment of nodes is divided into different stages, and the IGSO is used to solve the model in each stage. Through testing classical test functions and optimizing the problems of coverage in WSNS, the simulation results show that the IGSO achieves more reasonable results and can effectively provide the optimal solution of network coverage.