瞬态搜索金枪鱼优化城市物流无人机路径规划

Urban logistics UAV path planning based on transientsearch tuna optimization

针对复杂城市物流无人机路径规划易收敛于局部最优的不足,提出一种自适应瞬态搜索金枪鱼优化算法的无人机路径规划方法。设计Kent混沌初始化丰富初始种群多样性,提出自适应瞬态搜索平衡全局搜索与局部开发,利用透镜成像对立学习和柯西变异混合扰动防止算法陷入局部最优。利用改进金枪鱼优化算法求解城市物流无人机路径规划问题,结合城市飞行环境三维建模,设计评估路径规划的多目标代价函数,并对物流无人机路径规划方案迭代求解。实验结果表明,改进算法能够得到安全避障且代价更小的路径。

Aiming at the shortage of convergence to a local optimum in logistics UAV path planning in complex urban city,a UAV path planning method based on adaptive transient search tuna optimization algorithm was proposed.The Kent chaos initia-lization was designed to enrich the diversity of initial population,an adaptive transient search was presented to balance global search and local development.A hybrid perturbation strategy of lens imaging and Cauchy mutation was used to prevent the algorithm from falling into a local optimum.The improved tuna optimization algorithm was used to solve the urban logistics UAV path planning problem,combining with the three-dimensional modeling of the urban flight environment,the multi-objective cost function was constructed to evaluate the path planning,and the logistics UAV path planning scheme was iteratively solved.The results show that the improved algorithm can obtain a safe and low-cost path to avoid all obstacles.