Application of improved bee evolutionary genetic algorithm on vehicle routing problem with time window

A new method for solving vehicle routing problems with time-window (VRPTW) based on bee evolutionary genetic algorithm (BEGA) is proposed. By adding a delivery vehicle fixed cost to the fitness function, the contradictory between the number of vehicles and driving distance at the same time is solved effectivity. Self-adaptive crossover operator is adopted to increase the accuracy of optimization and reduce the probability of trapping in local optimum. The performances of the BEGA and other intelligent algorithms were compared by using the typical instances. The simulation results indicated that BEGA can improve the convergence speed under the same iterations because it use the optimum individual as a queen-bee in population for the parent select. On the other hand, BEGA has introduced a random population in order to extend search ability and maintain the population diversity.