Journal of Hebei University of Science and Technology (Feb 2022)
Research on vehicle routing problem based on improved ant colony algorithm[JP]
Abstract
In order to solve the problems of slow convergence and easiness to fall into local optimal solution in solving vehicle routing problem,an improved ant colony algorithm was proposed.Firstly,the saving matrix updating selection probability formula was introduced to guide ant search;Secondly,the piecewise function was used to improve the volatilization factor and adjust the convergence speed of the algorithm;Thirdly,the 2-opt method was used to improve the local search ability of the algorithm;Finally,the international general data set of vehicle routing problem was selected for simulation,and the control variable method was used to find the appropriate values of pheromone factor and heuristic function factor.The improvement effect of the algorithm was tested with class P data,and compared with basic ant colony algorithm,genetic algorithm,simulated annealing algorithm and particle swarm optimization algorithm.The results show that compared with the basic ant colony algorithm,the total length of the optimal path of the improved ant colony algorithm is reduced by [BF]6.97%[BFQ];Compared with genetic algorithm,simulated annealing algorithm and particle swarm optimization algorithm,the improved ant colony algorithm has stronger optimization ability and faster convergence speed.Therefore,the improved ant colony algorithm can effectively reduce the path length,jump out of the local optimization and accelerate the convergence speed.Especially in the case of a single route that allows more service points and discrete distribution of points,its advantages are more obvious,which provides a certain reference for solving the vehicle routing problem.[HQ]
