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

Abstract

The performance of improved glowworm swarm optimization (GSO) algorithm and its application in coverage optimization of WSNs are analyzed in this paper. The global convergence analysis of basic GSO is made. In order to improve the GSO convergence efficiency, an improved GSO (IGSO) is presented, and it is proved to be guaranteed to the global optimization with probability one. Further, a new coverage optimization algorithm for WSNS, based on IGSO, 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.

Keywords

• Artificial intelligence
• Wireless sensor network
• Glowworm swarm optimization algorithm
• Convergence
• Network coverage.