Abstract—Considering multi-depot vehicle routing problem (MDVRP) is widely used in actual life, mathematical model of
MDVRP was established. An improved ant colony
optimization (IACO) was proposed for solving this model.
Firstly, MDVRP was transferred into different groups according to nearest depot method, then constructing the initial route by scanning algorithm (SA). Secondly, genetic operators were introduced, and then adjusting crossover
probability and mutation probability adaptively in order to
improve the global search 1ability of the algorithm. Moreover, smooth mechanism was used to improve the performance of
ant colony optimization (ACO). Finally, 3-opt strategy was used to improve the local search ability. The proposed IACO has been tested on 6 MDVRP benchmark problems. The experimental results show that IACO is superior to ACO in
terms of convergence speed and solution quality, thus the proposed method is effective and feasible, and the proposed
model is meaningful.
Index Terms—Smulti-depot vehicle routing problem,
improved ant colony optimization, genetic algorithm, smooth
mechanism.
Yalian Tang is with Dongguan Jinan University Institute, China (e-mail: tangyalian11@163.com).
[PDF]
Cite: Tang Yalian, "An Improved Ant Colony Optimization for Multi-Depot
Vehicle Routing Problem," International Journal of Engineering and Technology vol. 8, no. 5, pp. 385-388, 2016.
