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| Dynamic Hybrid Ant Colony Optimization Algorithm for Solving the Vehicle Routing Problem with Time Windows |
| 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.GOCOM Information Technology Co., Ltd, Hefei 230088 |
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Abstract To solve the vehicle routing problem with time windows (VRPTW), a dynamic hybrid ant colony optimization algorithm (DHACO) is proposed, so as to avoid the disadvantages of traditional ant genetic hybrid algorithm, such as static setting, redundant iteration and slow convergence. Firstly, an initial solution is obtained through max-min ant system, and the ant colony optimization algorithm is adopted to get a basic feasible solution to VRPTW. Then, the crossing and mutation operations of genetic algorithm are employed to re-optimize local and global solutions, thus the optimal solution is obtained. Finally, based on the fusion strategy of ant genetic hybrid algorithm, and by employing ant algorithm and genetic algorithm dynamically and alternately, the parameters of ant colony algorithm is self-adaptively controlled according to cloud association rules. DHACO reduces the times of redundant iteration and speeds up the rate of the convergence. Simulation results show that DHACO is better than the other related heuristic algorithms as to the optimal solutions.
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Received: 18 August 2014
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2015, Vol. 28 
