Research On Improvement And Application Of Ant Colony Optimization Algorithm

Ant colony optimization(ACO)was first proposed by Italian scholars Dorigo in the 1990s.Many scholars have applied it in their own fields,such as medicine,logistics,network,etc.,and have made some achievements,and have solved many NP hard problems.With the wide application of mobile robot,its path planning has become a research hotspot.Therefore,it is of great significance to study the improvement of ant colony algorithm and its application in robot path planning.The main works are as follows:1.Two improved ant colony algorithms are combined,and a new improved ant colony optimization algorithm is proposed.In this algorithm,the distance from the next node to the final node is added to the heuristic function.At the same time,when the pheromone is updated,the performance of the algorithm is improved by increasing and reducing the pheromone concentration according to the optimal solution and the worst solution respectively.The improved ant colony algorithm is applied to robot path planning.Compared with the basic ant colony algorithm,it improves the convergence speed and the accuracy of the solution.2.A hybrid algorithm combined genetic algorithm with the improved ant colony algorithm is proposed for robot path planning.First of all,use genetic algorithm to get the optimal solution.Then,the path length of this optimal solution is added to the initial pheromone value of the improved ant colony algorithm as a part.Finally,use the improved ant colony algorithm to get the optimal path based on the feature of the high efficiency and the positive feedback.Comparing the hybrid algorithm with the improved ant colony algorithm,it is found that the hybrid algorithm can find the results faster than the improved ant colony algorithm,and the efficiency of the hybrid algorithm is verified.