将蚁群算法与人工势场算法相结合，提出了一种新的寻优算法。在算法的设计过程中，首先引入人工势场法进行蚁群算法初始信息素的分配，避免了在迭代初始阶段，信息素太少与启发信息不成比例而使得蚂蚁集中在启发信息最强的路径上，从而陷入局部最优的问题。其次，通过引入势场引导函数改进蚁群算法的状态转移函数，避免了在三维空间中蚂蚁搜索容易忽视节点周围障碍物因素，从而陷入盲目选择导致搜索时间过长的问题。将优化算法应用于无人机三维航迹规划问题的求解，并通过仿真验证了有效性。
A new optimization algorithm is proposed by combining the ant colony algorithm and the artificial potential field algorithm. In the design process of the algorithm, the artificial potential field method is first introduced to allocate the initial pheromone of the ant colony algorithm, thereby avoiding the problem of local optimization caused by concentration of ants on the path with the strongest heuristic information due to the disproportion of too few pheromones to the heuristic information at the initial stage of the iteration. Secondly, by introducing the potential field guiding function to improve the state transfer function of the ant colony algorithm, we avoid the problem of long search time caused by blind selection which results from the fact that the ant searches in 3D space and easily ignores the obstacles around the node. Finally, the optimization algorithm is applied to solve the UAV 3D path planning problem, and the effectiveness is verified by simulation.
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