A Novel Compact Particle Swarm Optimization for Optimizing Coverage of 3D in Wireless Sensor Network

Abstract

The problem of 3D coverage in a wireless sensor network (WSN) has always been an urgent problem to be solved. A novel compact particle swarm optimization algorithm (ncPSO) to solve this problem is proposed in this paper. This algorithm uses a Pareto distribution to describe the position of particle swarms. The ncPSO reduces memory usage compared to traditional heuristics. The ncPSO using the Pareto distribution is less likely to fall into local optima than other compact algorithms using the Gaussian distribution. We also add Gaussian perturbation strategy to the algorithm to better avoid the algorithm falling into local optimum. Among the test functions of CEC2013, the ncPSO achieves remarkable optimization ability on most test functions. Finally, we apply ncPSO to the 3D coverage problem of sensors. Compared with other algorithms, the ncPSO achieves satisfactory results.