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Application of hybrid evolutionary algorithm to single source capacitated warehouse location problem

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Warianty tytułu
Konferencja
Evolutionary Computation and Global Optimization (10; Krajowa Konferencja Algorytmy Ewolucyjne i Optymalizacja Globalna; 11-13.06.2007; Będlewo, Poland)
Języki publikacji
EN
Abstrakty
EN
This paper presents two-phase hybrid evolutionary algorithm (EA) to optimize Single Souece Capacitated Warehouse Location Problem (SSCWLP), a well-known location-allocation problem employed for the telecommunication network design modeling. The first phase of the algorithm aims the search to satisfy the problem constraints and during the second phase actual optimization take place. To improve the performance of EA the algorithm is combined with other local search heuristics. Influence of EA hybridization as well as different selection schemes dealing with constraint handling are discussed. In adition, performance of co-evolutionary algorithm (co-EA) versus the EA with single population is compared across a set of example problems.
Słowa kluczowe
Rocznik
Tom
Strony
125--133
Opis fizyczny
Bibliogr. 14 poz., wykr., tab.
Twórcy
Bibliografia
  • [1] Arabas, J. Wykłady z algorytmów ewolucyjnych, WNT, Warszawa 2001.
  • [2] Britain, D. Optimisation of the Telecommunications Access Network, Ph.D. Dissertation, University of Bristol, Department of Engineering Mathematics, 1999.
  • [3] Cortinhal, M. J., Capitivo, M. E. Genetic Algorithms for the Single Source Capacitated Location Problem: a Computational Study. 4-th Metaheuristic. Int. Conf., Porto, 2001.
  • [4] Deb, K., Agrawal, S. A fast elitist nondominated sorting genetic algorithm for multiobjective optimization: NSGA-II. In Proceedings of the Conference on Parallel Problem Solving From Nature VI, 2000, pp. 849-858.
  • [5] Deb, K., Srinivas, N. Multiobjective optimization using nondominated sorting in genetic algorithms. Journal of Evolutionary Computation, Vol. 2, No. 3, pages 221-248.
  • [6] Galvao, R.D. Uncapacitated facility location problems: Contributions. Pesquisa Operaciona, Janeiro a Abril de v. 24, n.1 , p.7-38, 2004.
  • [7] Garrett, D., Dasgupta, D. Analyzing the performance of hybrid evolutionary algorithms for the multiobjective quadratic assignment problem, 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, July 16-21, 2006.
  • [8] Gourdin, E., Labbe, M. Yamann H. Telecommunication and location, January 2001.
  • [9] Horng, J. T., Lin, L. Y., Liu, B. J., Kao, C. Y. Resolution of Simple Plant Location Problems Using an Adopted Genetic. Proc. Congress on Evol. Comp. CAC 99, 1999.
  • [10] Michalewicz, Z. Test-case generator for nonlinear continuous parameter optimization techniques. IEEE Transactions on Evolutionary Computation, Vol. 4, pp. 197-215, 2000.
  • [11] Scaparra, M. P., Ahuja, R.K., Orlin, J. B. A multi-exchange heuristic for the single source capacitated facility location problem. Technical report: TR-02-11, 2002.
  • [12] Scaparra, M. P., Scutella, M. G. Facilities, Locations, Customers: Building blocks of location models. A survey, Technical report: TR-01-18, 2001.
  • [13] Venkatraman, S., Yen, G. A generic framework for constrained optimization using genetic algorithms, IEEE Trans. on Evol. Comp. Vol. 9, No 4, August 2005.
  • [14] Zitzler, E., Deb, K., Thiele, L. Comparison of multiobjective evolutionary algorithms: Empirical results. Journ Evol. Comp. Vol. 8, No. 2, pp. 173-195, 2000.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA6-0040-0015
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