Optimization And Application Research Of Ant Colony Algorithm In Logistics Transportation

Ant Colony Optimization(ACO)is a meta heuristic algorithm based on the positive feedback behavior generated by the pheromone mechanism in the process of searching for food by ants in nature,thus forming the optimal path foraging behavior.It was proposed by Marco Dorigo et al.in 1992.Ant colony algorithm has the characteristics of strong search ability,strong robustness,simple structure and so on.With the continuous research in recent years,researchers have found that the algorithm has some shortcomings,such as slow convergence speed,large amount of calculation,easy to fall into local optimal value and poor solution quality due to improper setting of dependent parameters.This paper analyzes and improves the shortcomings of the ant colony algorithm,proposes three improved ant colony algorithms,and applies them to logistics transportation optimization problems to further improve the theoretical basis of ACO and improve the performance of the ACO algorithm.The main research work of this paper is as follows:In order to prevent the algorithm from falling into local optimum and improve the overall optimization ability of the algorithm,a Self-Adaptation Ant Colony Optimization(SACO)algorithm is proposed by introducing direction adjustment factor and deadlock prevention mechanism.SACO is applied to the path planning problem and compared with the traditional ant colony algorithm and the Max-Min Ant System(MMAS).The experimental results show that SACO has strong competitiveness in the robot path planning problem.In order to improve the solution accuracy and convergence speed of the ant colony algorithm,make it produce the next generation population with better performance,and accelerate the global optimization.Based on the ant colony algorithm,a hybrid ant colony algorithm(AC-GA)is proposed by introducing some operations of the genetic algorithm.AC-GA is used to solve the traveling salesman and vehicle scheduling problem,and its performance is tested with a public dataset.The test results are compared with the results of other algorithms and existing literature,and the results show that the hybrid algorithm has a high accuracy in solving such problems,and in some aspects,the performance is due to other algorithms.In order to improve the performance of the basic ACO algorithm and prevent it from falling into the local optimal solution.A greedy strategy is introduced to adjust the relative importance of heuristic information and dynamically change the diversity of the population as the number of iterations increases.A Greedy Ant Colony Optimization(GACO)algorithm is proposed to solve the location problem of multiple distribution centers.By solving an actual case and comparing the solution results with the other four algorithms,the results show that the convergence speed and solution accuracy of GACO are superior to those of other comparison algorithms.The simulation results show that the algorithm is feasible and superior.