A Novel Modified Ant Colony Optimization Algorithm by Resetting and Updating Pheromone for Vehicle Routing Problem with Time Windows

Abstract

Ant Colony Optimization (ACO) algorithm is used for solving combinational optimization problem. Vehicle routing problem with time windows (VRPTW) is a well-known combinatorial problem. Many researchers apply ACO to solve VRPTW. However, Applying ACO with VRPTW encounters the problem of trapping in local optimum. The effect of trapping in local optimum originates from the pheromone of ants. This paper proposes improving ACO by resetting pheromone when trapping in local optimum occurs. The resetting pheromone is similar to run again. The results from the previous round searching are useless. Hence, this paper proposes the result from the previous round searching apply with the pheromone to guide the searching next round. The proposed algorithm was tested on fifty-six maps from a series of Solomon and provided more satisfactory results in comparison with other ACO algorithms.