Optimisation and application research of ant colony algorithm in vehicle routing problem

In this paper, an improved ant colony algorithm based on ant system is proposed in order to solve vehicle routing problem. When choosing the path, the 2-opt method is used to explore the reasonable selection of the parameters of the algorithm for vehicle routing problem taking the path savings among customers as heuristic information. The performance of ant colony algorithm is affected by the information heuristic factor alpha, expectation heuristic factor beta and pheromone volatile factor rho. The method breaks through empirically setting the ant colony algorithm parameter values. By calculation, the optimal parameters of the ant colony algorithm in solving the vehicle routing problem are: alpha epsilon [1.0, 1.7], beta epsilon [4.5, 8.5], rho epsilon [0.5, 0.6]. At last, an exploration is established to find the optimal solution by combining three parameters and the ant colony algorithm will have a better effect in the actual optimisation problem.