Path planning of complex environment based on hyper view ant colony algorithm

A hyper view ant colony algorithm is developed to deal with the issues of sluggish convergence and poor search ability of traditional ant colony algorithms in complex environments. The concept of hyper view ant and view selection mechanism are first introduced in the enhanced ant colony algorithm. Dijkstra algorithm is used to determine the optimal path for the reachable node set of hyper view ants, where the state transition is accomplished using the designed pheromone calculation method. In addition, this paper creates an ant knowledge database and introduces it into the state transition type, which makes the information communication between ants more adequate. The knowledge database will be updated via historical path, and its value will be adaptively loaded. Then, an ant view atrophy mechanism is developed to balance the time efficiency of the proposed algorithm, and a pheromone compensation method is given to ensure the adsorption of algorithm to optimal path. Finally, by the experiments in various complex environments, the statistics of different performance parameters show that the results of the proposed algorithm in this paper are better than the ones of the existing algorithms including traditional ant colony algorithm.