Dynamic Adaptive Ant Colony Optimization Algorithm for Min-Max Vehicle Routing Problem
GE Bin1,2, HAN Jiang-Hong1, WEI Zhen3, CHENG Lei3, HAN Yue2
1.School of Computer and Information, Hefei University of Technology, Hefei 230009 2.College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001 3.GO COM Information Technology Co., Ltd, Hefei 230088
Abstract：To solve the min-max vehicle routing problem (MMVRP), a dynamic adaptive ant colony optimization algorithm is proposed. The dynamic max-min ant system is adopted to adjust the optimal solution. τmin is updated per iteration, it is regarded as the function of maximum in the pheromone matrix, and the probability of selecting arc is adjusted according to the optimal arc. A kind of gray model is employed to forecast and control the boundary of pheromone matrix to enhance the self-adaption of parameters in ant colony algorithm. Advantage of pheromone associated with accumulation rules is taken to update multiple nodes with relatively high concentration of pheromone and edges nearby. The proposed algorithm is tested on examples. The simulation results show that compared with linear programming algorithm and other related ant colony algorithms, the proposed algorithm has a higher convergence speed and better optimization performance and applicability.
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2015, Vol. 28 
