Abstract
Grain is an important economic and strategic material of the country. In grain transportation, it is necessary to consider the running time, vehicle number, path length and other factors at the same time, which is a typical multi-objective problem, but also a NP-Hard problem. In this paper, an Artificial Bee Colony algorithm is introduced to solve the routing problem of grain transportation vehicles with multi-objective and time windows. Combined with the practical problems of grain transportation, the standard Artificial Bee Colony algorithm is improved in four aspects: population initialization, domain search mode, bulletin board setting and scout bee search mode, and a Multi-objective Artificial Bee Colony algorithm is proposed by using the strategy of first classification and then sorting and final iteration. The proposed algorithm is compared with other algorithms by using the standard test set in Solomon database. The results show that the Multi-objective Discrete Artificial Bee Colony algorithm has great advantages in solving the routing problem of grain transportation vehicles with time windows.
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References
Dantzig, G., Ramser, J.: The truck dispatching problem. Manag. Sci. 6, 80–91 (1959)
Lang, M., Hu, S.: Research on solving logistics distribution path optimization problem with hybrid genetic algorithm. Chin. Manag. Sci. 10(5), 51–56 (2002)
Lv, X., Liao, T.: Research on postal vehicle routing problem with time window based on genetic algorithm. J. Shandong Univ. 06(44), 46–50 (2009)
Lin, F., Guo, H.: Simulation research on workshop distribution path optimization based on ant colony algorithm. Mech. Des. Manuf. 10(10), 13–15 (2007)
Tang, Y., Liu, F.: A new genetic simulated annealing algorithm for solving VRPTW problem. Comput. Eng. Appl. 42(7), 12–14 (2006)
Karaboga, D.: An idea based on honey bee swarm for numercial optimization. Technical Report-TR06. Erciyes University, 13–15 (2005)
Jin, Y., Sun, Y., Wang, J., Wang, D.: An improved elite artificial bee colony algorithm based on simplex. J. Zhengzhou Univ. 39(6), 13–15 (2018)
Zhao, Y., Xu, X., Huang, W., Ma, Y.: Hybrid artificial bee swarm algorithm based on cat swarm idea. Comput. Technol. Dev. 29(1), 11–12 (2019)
Liang, X., Zhao, X.: An improved artificial bee swarm algorithm based on steepest drop method. J. Beijing Univ. Archit. 34(3), 49–56 (2018)
Chao, X., Li, W.: Feature selection method for artificial bee swarm algorithm optimization. Comput. Sci. Explor. 13(2), 300–309 (2019)
Aslan, S.: A transition control mechanism for artificial bee colony algorithm. Comput. Intell. Neurosci. 4(6), 1–23 (2019). https://doi.org/10.1155/2019/5012313
Dervis, K.: Discovery of conserved regions in DNA sequences by Artificial Bee Colony (ABC) algorithm based methods. Nat. Comput. 15(6) (2019). https://doi.org/10.1007/s11047-018-9674-1
Yu, X.: Research on vehicle routing problem with time window considering carbon emission based on artificial bee swarm algorithm. Master’s thesis. Dalian University of Technology, vol. 1, no. 5, pp. 88–89 (2016)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
Gong, M., Jiao, L., Yang, D.: Research on evolutionary multiobjective optimization algorithm. J. Softw. 20(2), 271–289 (2009)
Alzaqebah, M., Abdullah, S., Jawarneh, S.: Modified artificial bee colony for the vehicle routing problems with time windows. SpringerPlus 5, 1298 (2016)
Tan, K.C., Lee, L.H., Zhu, Q.L., Ou, K.: Heuristic methods for vehicle routing problem with time windows. Artif. Intell. Eng. 15, 281–295 (2001)
Yu, B., Yang, Z.Z., Yao, B.Z.: A hybrid algorithm for vehicle routing problem with time windows. Expert Syst. Appl. 38, 435–441 (2011)
Su, X., Sun, H., Pan, X.: Simulation of traveling Salesman problem based on improved bee swarm algorithm. Comput. Eng. Des. 34(4), 1420–1424 (2013)
Acknowledgments
The work was supported by National Natural Science Foundation of China (Grant No. 61179032 and 61303116), the Special Scientific Research Fund of Food Public Welfare Profession of China (Grant No. 2015130043), the Research and Practice Project of Graduate Education Teaching Reform of Polytechnic University (YZ2015002), the Scientific research project of Wuhan Polytechnic University (2019), Key Project of Philosophy and Social Science Research Project of Hubei Provincial Department of Education in 2019(19D59), Science and Technology Research Project of Hubei Provincial Department of Education (D20191604).
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Liang, W., Liu, S., Zhou, K., Fan, S., Shang, X., Yang, Y. (2020). Improved Discrete Artificial Bee Colony Algorithm. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_46
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DOI: https://doi.org/10.1007/978-981-15-3425-6_46
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