【摘要】 针对启发式算法在无人机路径规划中存在收敛精度低以及容易陷入局部最优的问题，提出了一种自适应鱼鹰优化算法。该算法首先利用Bernoulli混沌映射初始化种群，增加种群多样性；其次引入余弦自适应因子平衡全局搜索和局部开发能力，并结合莱维飞行策略自适应调整步长，帮助鱼鹰个体更好地跳出局部最优；接着通过折射反向学习策略改善全局最优解的质量，提高收敛精度和速度；然后将其与其他5种算法在15个CEC2005测试函数中进行性能对比实验，结果表明该算法在收敛精度和稳定性方面表现出色；最后将其移植应用于无人机路径规划问题，在6峰、9峰和12峰的地形障碍模型下进行测试。仿真结果显示，在不同地形场景下自适应鱼鹰优化算法较其他算法平均代价更低、标准差更小，且生成的路径更短、更平稳。更多还原

【Abstract】 Aiming at the problems of low convergence accuracy and local optimization of heuristic algorithm in Unmanned Aerial Vehicle(UAV)path planning,an Adaptive Osprey Optimization Algorithm(AOOA) is proposed.Firstly,Bernoulli chaotic mapping is used to improve the population diversity effectively.Secondly,adaptive cosine factor is introduced to balance the global search ability and local development ability,and in combination with Levy flight strategy,the step size is adjusted adaptively to help the individual osprey better jump out of the local optimal.Then,the refraction reverse learning strategy is used to improve the quality of the global optimal solution,and the convergence accuracy and speed are improved to a certain extent.After that,the performance of the algorithm is compared with that of other 5 algorithms in 15 CEC2005 test functions,and the results show that the algorithm has excellent performance in convergence accuracy and stability.Finally,it is transplanted to the UAV path planning problem and tested under the terrain obstacle models with 6,9 and 12 peaks.The simulation results show that,compared with other algorithms,AOOA has lower average cost,lower standard deviation,and shorter and more stable path in different terrain scenarios.更多还原