\r\nhave been widely used since two decades. In most of the literature,

\r\nan ant is a constructive heuristic able to build a solution from scratch.

\r\nHowever, other types of ant algorithms have recently emerged: the

\r\ndiscussion is thus not limited by the common framework of the

\r\nconstructive ant algorithms. Generally, at each generation of an ant

\r\nalgorithm, each ant builds a solution step by step by adding an

\r\nelement to it. Each choice is based on the greedy force (also called the

\r\nvisibility, the short term profit or the heuristic information) and the

\r\ntrail system (central memory which collects historical information of

\r\nthe search process). Usually, all the ants of the population have the

\r\nsame characteristics and behaviors. In contrast in this paper, a new

\r\ntype of ant metaheuristic is proposed, namely SMART (for Solution

\r\nMethods with Ants Running by Types). It relies on the use of different

\r\npopulation of ants, where each population has its own personality.","references":"[1] N. Zufferey, \u201cMetaheuristics: some Principles for an Efficient Design,\u201d\r\nComputer Technology and Applications, vol. 3 (6), pp. 446 \u2013 462, 2012.\r\n[2] M. Gendreau and J.-Y. Potvin, Handbook of Metaheuristics, ser.\r\nInternational Series in Operations Research & Management Science.\r\nSpringer, 2010, vol. 146.\r\n[3] M. Dorigo, \u201cOptimization, learning and natural algorithms (in Italian),\u201d\r\nPh.D. dissertation, Politecnico di Milano, Dipartimento di Elettronica,\r\nItaly, 1992.\r\n[4] C. Blum, \u201cAnt colony optimization: Introduction and recent trends,\u201d\r\nPhysics of Life Reviews, vol. 2(4), pp. 353\u2013373, 2005.\r\n[5] M. Dorigo, M. Birattari, and T. Stuetzle, \u201cAnt colony optimization\r\n\u2013 artificial ants as a computational intelligence technique,\u201d IEEE\r\nComputational Intelligence Magazine, vol. 1 (4), pp. 28\u201339, 2006.\r\n[6] M. Dorigo and T. Stuetzle, Handbook of Metaheuristics. In F.\r\nGlover and G. Kochenberger (Eds), 2003, vol. 57, ch. The Ant Colony\r\nOptimization Metaheuristic: Algorithms, Applications, and Advances,\r\npp. 251\u2013285.\r\n[7] N. Zufferey, \u201cOptimization by ant algorithms: Possible roles for an\r\nindividual ant,\u201d Optimization Letters, vol. 6 (5), pp. 963 \u2013 973, 2012.\r\n[8] \u201cDesign and classification of ant metaheuristics,\u201d in Proceedings of the\r\n22nd Euromicro International Conference on Parallel, Distributed and\r\nNetwork-Based Processing (PDP 2014), Turin, Italy, February 12 \u2013 14\r\n2014.\r\n[9] L. Luyet, S. Varone, and N. Zufferey, \u201cAn Ant Algorithm for the Steiner\r\nTree Problem in Graphs,\u201d Lecture Notes in Computer Science, vol. 4448,\r\npp. 42 \u2013 51, 2007.\r\n[10] N. Zufferey, J. Farres, and R. Glardon, \u201cAnt metaheuristics with adapted\r\npersonalities for the vehicle routing problem,\u201d in Proceedings of the\r\n6th International Conference on Computational Logistics (ICCL 2015),\r\nDelft, Nederland, September 23 \u2013 25 2015.\r\n[11] M. Plumettaz, D. Schindl, and N. Zufferey, \u201cAnt local search and its\r\nefficient adaptation to graph colouring,\u201d Journal of the Operational\r\nResearch Society, vol. 61, pp. 819 \u2013 826, 2010. [12] A. Hertz, D. Schindl, and N. Zufferey, \u201cLower bounding and tabu search\r\nprocedures for the frequency assignment problem with polarization\r\nconstraints,\u201d 4OR, vol. 3 (2), pp. 139 \u2013 161, 2005.\r\n[13] J.-F. Cordeau, M. Gendreau, A. Hertz, G. Laporte, and J.-S. Sormany,\r\nLogistics Systems: Design and Optimization. Springer, 2005, ch. New\r\nHeuristics for the Vehicle Routing Problem, pp. 270\u2013297.\r\n[14] J.-F. Cordeau, M. Gendreau, G. Laporte, J.-Y. Potvin, and F. Semet,\r\n\u201cA Guide to Vehicle Routing Heuristics,\u201d Journal of the Operational\r\nResearch Society, vol. 53 (5), pp. 512\u2013522, 2002.\r\n[15] J.-F. Cordeau and G. Laporte, Metaheuristic Optimization via Memory\r\nand Evolution: Tabu Search and Scatter Search. Boston: Kluwer, 2004,\r\nch. Tabu search heuristics for the vehicle routing problem, pp. 145\u2013163.\r\n[16] M. Gendreau, G. Laporte, and J.-Y. Potvin, The Vehicle Routing\r\nProblem. Philadelphia: SIAM Monographs on Discrete Mathematics\r\nand Applications, 2002, ch. Metaheuristics for the VRP, pp. 129\u2013154.\r\n[17] B. L. Golden, E. A. Wasil, J. P. Kelly, and I.-M. Chao, Fleet Management\r\nand Logistics. Boston: Kluwer, 1998, ch. Metaheuristics in vehicle\r\nrouting, pp. 33\u201356.\r\n[18] G. Laporte and F. Semet, The Vehicle Routing Problem. Philadelphia:\r\nSIAM Monographs on Discrete Mathematics and Applications, 2002,\r\nch. Classical heuristics for the capacitated VRP, pp. 109\u2013128.\r\n[19] S. Lin, \u201cComputer solutions of the traveling salesman problem,\u201d Bell\r\nSystem Technical Journal, vol. 44, pp. 2245\u20132269, 1965.\r\n[20] I. Or, \u201cTraveling salesman-type combinatorial problems and their\r\nrelation to the logistics of regional blood banking,\u201d Ph.D. dissertation,\r\nNortwester University, USA, 1976.\r\n[21] N. Christofides, A. Mingozzi, and P. Toth, Combinatorial Optimization,\r\n1979, ch. The vehicle routing problem, pp. 315 \u2013 338.\r\n[22] D. Mester and O. Braysy, \u201cActive-guided evolution strategies for\r\nlarge-scale capacitated vehicle routing problems,\u201d Computers &\r\nOperations Research, vol. 34 (10), pp. 2964 \u2013 2975, 2007.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 109, 2016"}