Abstract
In this paper, we focus on the importance of examining characteristics of non-dominated solutions especially when a user should select only one solution from non-dominated solutions at a time, and select another solution due to the change of problem conditions. Although he can select any solution from non-dominated solutions, the similarity of selected solutions should be considered in practical cases. We show simulation results on vehicle routing problems that have two demands of customers: Normal Demand Problem (NDP) and High Demand Problem (HDP). In our definition the HDP is an extended problem of NDP. We examined two ways of applying an EMO algorithm. One is to apply it to each problem independently. The other is to apply it to the HDP with initial solutions generated from non-dominated solutions for the NDP. We show that the similarity of the obtained sets of non-dominated solutions is enhanced by the latter approach.
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References
Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.): EMO 2001. LNCS, vol. 1993. Springer, Heidelberg (2001)
Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.): Proc. of Second International Conference on Evolutionary Multi-Criterion Optimization, EMO2003 (2003)
Deb, K.: Applications of multi-objective evolutionary algorithms. In: Multi-objective Optimization Using Evolutionary Algorithms, pp. 447–479. John Wiley & Sons, England (2001)
Tavares, J., Pereira, F.B., Machado, P., Costa, E.: Crossover and diversity: A study about GVR. In: Proc. of AdoRo Workshop held at GECCO, 7 pages (2003)
Berger, J., Barkaoui, M.: A hybrid genetic algorithm for the capacitated vehicle routing problem. In: Proc. of GECCO 2003, pp. 646–656 (2003)
Tan, K.C., Lee, T.H., Chew, Y.H., Lee, L.H.: A hybrid multiobjective evolutionary algorithms for solving vehicle routing problem with time windows. In: Proc. of IEEE International Conf. on Systems, Man, and Cybernetics 2003, Washington D.C., U.S.A, October 5-8, 2003, pp. 361–366 (2003)
Saadah, S., Ross, P., Paechter, B.: Improving vehicle routing using a customer waiting time colony. In: Proc. of 4th European Conf. on Evolutionary Computation in Combinatorial Optimization, pp. 188–198 (2004)
Chitty, D.M., Hernandez, M.L.: A hybrid ant colony optimisation technique for dynamic vehicle routing. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 48–59. Springer, Heidelberg (2004)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)
Lenstra, J.K., Rinnooy Kan, A.H.G.: Complexity of vehicle routing and scheduling problems. Networks 11, 221–227 (1981)
Oliver, I.M., Smith, D.J., Holland, J.R.C.: A study of permutation crossover operators on the traveling salesman problem. In: Proc. of 2nd International Conference on Genetic Algorithm and Their Application, pp. 224–230 (1987)
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Murata, T., Itai, R. (2005). Multi-objective Vehicle Routing Problems Using Two-Fold EMO Algorithms to Enhance Solution Similarity on Non-dominated Solutions. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_61
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DOI: https://doi.org/10.1007/978-3-540-31880-4_61
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