DOI QR코드

DOI QR Code

Multi-vehicle Route Selection Based on an Ant System

  • Kim, Dong-Hun (Department of Electrical Engineering, Kyungnam University)
  • 발행 : 2008.03.01

초록

This paper introduces the multi-vehicle routing problem(MRP) which is different from the traveling sales problem(TSP), and presents the ant system(AS) applied to the MRP. The proposed MRP is a distributive model of TSP since many vehicles are used, not just one salesman in TSP and even some constraints exist. In the AS, a set of cooperating agents called vehicles cooperate to find good solutions to the MRP. To make the proposed MRP extended more, Tokyo city model(TCM) is proposed. The goal in TCM is to find a set of routes that minimizes the total traveling time such that each vehicle can reach its destination as soon as possible. The results show that the AS can effectively find a set of routes minimizing the total traveling time even though the TCM has some constraints.

키워드

참고문헌

  1. M. Dorigo , V. Maniezzo, A. Colorni , Ant system: optimization by a colony of cooperating agents, IEEE Trans. on Systems, Man, and Cybernatics-Part B 26(1996), 29-41 https://doi.org/10.1109/3477.484436
  2. M. Dorigo, E. Bonabeau, G. Theraulaz, Ant algorithms and stigmergy, Future Generation Computer Systems 16(2000), 851-871 https://doi.org/10.1016/S0167-739X(00)00042-X
  3. A. Colorni, M. Dorigo, V. Maniezzo, Distributed optimization by and colonies, In Proc. ECAL91-Eur. Conf. Artificial Life (1991), 134-142
  4. a. Colorni,M.Dorigo,V.Maniezzo, An investigation of somepropertiesofanantalgorithm, InProc.Paral- lel ProblemSolvingfromNatureConference(1992), 509-520
  5. P.Wei,W.Xiong,J.Zhao, An improved ant colony algorithm for TSP, World Congress on Intelligent Control and Automation 3(2004), 2263-2267
  6. X. Song,B.Li,H.Yang, Improved ant colony algorithm and its applications in TSP, Int. Conf. on Intelligent Systems Design and Applications 2(2006), 1145-1148
  7. A. Agarwal, M. H. Lim, M. J. Er, C. Y. Chew, ACO for a new TSP in region coverage, IEEE/RSJ Int. Conf. on Intelligent Robots and Systems(2005), 1717-1722
  8. J. Ouyang, G. R. Yan, A multi-group ant colony system algorithm for TSP, Proceedings of 2004 Int. Conf. on Machine Learning and Cybernetics 1(2004), 117-121
  9. F. A. Mohammadi, A. H. Fathi, M. T. Manzurit, Optimizing ACS for big TSP problems distributing ant parameters, Int. Symp. on Communications and Information Technologies(2006), 839-842
  10. J. Pan,D.Wang, An ant colony optimization algorithm for multiple travelling salesman problem innovative computing, information and control, Int. Conf. on Innovative Computing, Information and Control 1(2006), 210-213
  11. M. Dorigo, L. M. Gambardella, Ant colonysystem:a cooperative learning approach to the traveling salesman problem, IEEE Trans. on Evolutionary Computation 1(1997), 53-66 https://doi.org/10.1109/4235.585892
  12. M. Dorigo, Optimization, learningandnatutralalgo- ritms, Ph.D.dissertation, DEI, Politecnico di Milano, Italy (1992)
  13. J. C.Latombe, Robot Motion Planning, Boston, MA:Kluer (1991)
  14. M. Dorigo, Optimaization, learning and natural algoritms, Ph.D. dissertation, DEI, Politecnico di Milano, Italy (1992)
  15. M. Dorigo and T. Stutzle, Ant Colony Optimization, The MIT Press(2004)
  16. M. Dorigo, T. Stutzle, A short convergence proof for a class of and colony optimization algorithms, IEEE Trans. on Evolutionary Computation 6(2002), 358-365 https://doi.org/10.1109/TEVC.2002.802444
  17. M. X. Goemans and D. P. Williamson, 0.878-approximation algorithm for MAX-CUT and MAX-2SAT, Proceedings of the 26th Annual ACM Symposium on the Theory of Computing(1994), 422-431
  18. M. X. Goemans and D. P. Williamson, Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming, J. ACM 42(1995), 1115-1145 https://doi.org/10.1145/227683.227684