A Novel Hybrid Algorithm Based on Improved Particle Swarm Optimization Algorithm and Genetic Algorithm for Multi-UAV Path Planning with Time Windows

Abstract

In forest environment monitoring, data collection from wireless sensor networks may be separated from each other due to different geographical factors, which poses great difficulties for wireless sensor data collection. Therefore, this paper proposes a multi-target unmanned aerial vehicle (UAV) path planning method with time window to solve the path planning problem of data collection using UAV-assisted wireless sensor networks (WSN). In this paper, considering the energy consumption of the UAV and the time window of data collection, a multi-objective UAV path planning model is constructed. For this problem model, this paper, based on improved particle swarm optimization algorithm and genetic algorithm, proposes a hybrid algorithm (IPSO-GA) to plan the path of UAV data acquisition reasonably. Therefore, simulation experiments are conducted in order to evaluate the performance of this algorithm. The experimental results show that the hybrid algorithm (IPSO-GA) based on improved particle swarm optimization algorithm and genetic algorithm, which has been proposed in this paper can successfully generate an effective path, which covers all data nodes with the least number of UAVs. Besides, the IPSO-GA is superior to particle swarm optimization (PSO), improved particle swarm optimization (IPSO) and genetic algorithm (GA) in the global optimal value, the number of UAVs and the flying distance of UAVs.