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Parallel Multi-objective Memetic Algorithm for Competitive Facility Location

  • Algirdas Lančinskas
  • Julius Žilinskas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8385)

Abstract

A hybrid genetic algorithm for global multi-objective optimization is parallelized and applied to solve competitive facility location problems. The impact of usage of the local search on the performance of the parallel algorithm has been investigated. An asynchronous version of the parallel genetic algorithm with the local search has been proposed and investigated by solving competitive facility location problem utilizing hybrid distributed and shared memory parallel programming model on high performance computing system.

Keywords

Facility location Multi-objective optimization Memetic algorithms 

Notes

Acknowledgments

This research was funded by a Grant (No. MIP-063/2012) from the Research Council of Lithuania.

References

  1. 1.
    Friesz, T.L., Miller, T., Tobin, R.L.: Competitive networks facility location models: a survey. Pap. Reg. Sci. 65, 47–57 (1998)CrossRefGoogle Scholar
  2. 2.
    Plastria, F.: Static competitive facility location: an overview of optimisation approaches. Eur. J. Oper. Res. 129(3), 461–470 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    ReVelle, C.S., Eiselt, H.A., Daskin, M.S.: A bibliography for some fundamental problem categories in discrete location science. Eur. J. Oper. Res. 184(3), 817–848 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Huff, D.L.: Defining and estimating a trade area. J. Mark. 28, 34–38 (1964)CrossRefGoogle Scholar
  5. 5.
    Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007)zbMATHGoogle Scholar
  6. 6.
    Schaffer, J.D., Grefenstette, J.J.: Multi-objective learning via genetic algorithms. In: Proceedings of the 9th International Joint Conference on Artificial Intelligence, IJCAI’85, vol. 1, pp. 593–595. Morgan Kaufmann, San Francisco (1985)Google Scholar
  7. 7.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRefGoogle Scholar
  8. 8.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100 (2001)Google Scholar
  9. 9.
    Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)CrossRefGoogle Scholar
  10. 10.
    Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2, 221–248 (1994)CrossRefGoogle Scholar
  11. 11.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)CrossRefGoogle Scholar
  12. 12.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical Report C3P 826, California Institute of Technology (1989)Google Scholar
  13. 13.
    Mitra, K., Deb, K., Gupta, S.K.: Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithms. J. Appl. Polym. Sci. 69(1), 69–87 (1998)CrossRefGoogle Scholar
  14. 14.
    Weile, D.S., Michielssen, E., Goldberg, D.E.: Genetic algorithm design of Pareto optimal broadband microwave absorbers. IEEE Trans. Electromagn. Compat. 38(3), 518–525 (1996)CrossRefGoogle Scholar
  15. 15.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN VI. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)Google Scholar
  16. 16.
    Lančinskas, A., Žilinskas, J., Ortigosa, P.M.: Local optimization in global multi-objective optimization algorithms. In: 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 323–328. doi: 10.1109/NaBIC.2011.6089613 (2011)
  17. 17.
    Lančinskas, A., Ortigosa, P.M., Žilinskas, J.: Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm. Nonlinear Anal. Model. Control 18(3), 293–313 (2013)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Branke, J., Schmeck, H., Deb, K., Reddy, S.M.: Parallelizing multi-objective evolutionary algorithms: cone separation. In: CEC2004 Congress on Evolutionary Computation, vol. 2, pp. 1952–1957 (2004)Google Scholar
  19. 19.
    Deb, K., Zope, P., Jain, S.: Distributed computing of Pareto-optimal solutions with evolutionary algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 534–549. Springer, Heidelberg (2003) Google Scholar
  20. 20.
    Streichert, F., Ulmer, H., Zell, A.: Parallelization of multi-objective evolutionary algorithms using clustering algorithms. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 92–107. Springer, Heidelberg (2005) Google Scholar
  21. 21.
    Lančinskas, A., Žilinskas, J.: Approaches to parallelize Pareto ranking in NSGA-II algorithm. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011, Part II. LNCS, vol. 7204, pp. 371–380. Springer, Heidelberg (2012) Google Scholar
  22. 22.
    Lančinskas, A., Žilinskas, J.: Solution of multi-objective competitive facility location problems using parallel NSGA-II on large scale computing systems. In: Manninen, P., Öster, P. (eds.) PARA 2012. LNCS, vol. 7782, pp. 422–433. Springer, Heidelberg (2013) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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