基于PSO-GA算法的无人机集群森林火灾探查方法

UAV Cluster Forest Fire Detection Method Based on PSO-GA Algorithm

在航空护林领域,应用无人机进行森林火灾探查存在探查效率低、智能化程度低的困境。通过利用森林火灾烟雾蔓延的特性,结合无人机集群的优势,使得无人机集群可以根据火灾现场烟雾情况进行智能火源探查,同时在无人机集群探查算法方面在惯性权重线性减小、融合算法及森林风特征三方面进行优化,提出融合粒子群算法与遗传算法特性的PSO-GA算法,避免算法陷入局部最优,提高了无人机集群对森林火灾的探查效率及稳定性。仿真结果证明利用无人机集群根据烟雾浓度进行森林着火点探查的有效性,实验结果表明PSO-GA算法相比于传统粒子群算法及鱼群算法具有更好的寻优性与收敛性,缩短了森林着火点的探查时间。上述研究可为森林火灾探查提供有效支持,有效预防森林火灾的扩散。

In the field of aerial forest protection,the application of UAVs for forest fire detection suffers from the dilemma of low detection efficiency and low intelligence.By taking advantage of the smoke spreading characteristics of forest fires and combining the advantages of UAV clusters,the UAV clusters can conduct intelligent fire detection according to the smoke situation at the fire site,and at the same time,the UAV cluster detection algorithm is optimized in three aspects:linear reduction of inertia weights,fusion algorithm and forest wind characteristics.This paper proposes PSO-GA algorithm that combines the features of particle swarm algorithm and genetic algorithm.The proposed particle swarm genetic algorithm avoids the algorithm to fall into local optimum,and improves the efficiency and stability of UAV cluster for forest fire detection.The simulation results prove the effectiveness of forest fire detection by UAV cluster based on smoke concentration,and the experimental results show that PSO-GA algorithm has better optimality and convergence than traditional particle swarm algorithm and fish swarm algorithm,and shortens the detection time of forest fire.The above research can provide effective support for forest fire detection and prevent the spread of forest fires effectively.