【摘要】 为了实现鲸鱼优化算法(whale optimization algorithm, WOA)的种群多样性、减小计算复杂度，构造具有搜索上下界的初始种群。设计动态收敛因子和动态权重因子，以提高算法的收敛速度和计算精度，在此基础上，提出基于改进动态因子的鲸鱼优化算法并证明了其收敛性，分析了其复杂度。为了验证新算法优化性能和普适性，将改进的鲸鱼优化算法与其他优化算法进行比较，并将其应用到无人机(unmanned aerial vehicle, UAV)路径规划中。结果表明：基于改进动态因子的鲸鱼优化算法相比于其他优化算法有更好的收敛精度和更快的收敛速度。可见，基于改进动态因子的鲸鱼优化算法性能更好，能更高效的完成任务。更多还原

【Abstract】 In order to realize the diversity of whale optimization algorithm(WOA) and reduce the computational complexity, an initial population with upper and lower search bounds was constructed. Dynamic convergence factor and dynamic weight factor were designed to improve the convergence speed and calculation accuracy of the algorithm. On this basis, a whale optimization algorithm based on improved dynamic factor was proposed and its convergence was proved, and its complexity was analyzed. In order to verify the optimization performance and universality of the new algorithm, the improved whale optimization algorithm was compared with other optimization algorithms and applied to unmanned aerial vehicle(UAV) path planning. The results show that the whale optimization algorithm based on improved dynamic factors has better convergence accuracy and faster convergence speed than other optimization algorithms. It can be seen that the whale optimization algorithm based on the improved dynamic factor has better performance and can complete the task more efficiently.更多还原