基于改进蜂群算法的无人机路径规划

The UAV Path Planning Based on Improved Artificial Bee Colony Algorithm

人工蜂群算法存在初始蜜源具有随机性,后期收敛速度慢,易早熟等问题。运用最大最小距离积法处理初始蜜源,把K均值聚类算法与人工蜂群算法相结合,提出改进的人工蜂群算法,用于解决无人机路径规划问题。仿真结果表明改进的人工蜂群算法收敛速度更快,得到的解的适应度更好。

There are some problems in artificial bee colony algorithm,such as randomness of initial nectar source,slow convergence rate in late period and precocity,etc.An improved artificial bee colony algorithm is proposed to solve the problem of UAV path planning by combining k-mean clustering algorithm and artificial bee colony algorithm with the Max-Min distance clustering to deal with the initial nector source.The simulation results show that the improved artificial bee colony algorithm converges faster and the adaptivity of the obtained solution is better.