Implementation of a parallel genetic algorithm on a cluster of workstations: The Travelling Salesman Problem, a case study

  • Giuseppe Sena
  • Germinal Isern
  • Dalila Megherbi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1586)

Abstract

A parallel version of a Genetic Algorithm is presented and implemented on a cluster of workstations. Even through our algorithm is general enough to be applied to a wide variety of problems, we used it to obtain optimal/suboptimal solutions to the well known Traveling Salesman Problem. The proposed algorithm is implemented using the Parallel Virtual Machine library over a network of workstations, and it is based on a master-slave paradigm and a distributed-memory approach. Tests were performed with clusters of 1, 2, 4, 8, and 16 workstations, using several real problems and population sizes. Results are presented to whow how the performance of the algorithm is affected by variations on the number of slaves, population size, mutation rate, and mutation interval. The results presented show the utility, efficiency and potential value of the proposed algorithm to tackle similar NP-Complete problems.

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References

  1. [1]
    T.E. Anderson, D.E. Culler, D.A. Patterson and the NOW team A Case of NOW (Networks of Workstations). IEEE Micro, 15(1):54–64, February 1995.CrossRefGoogle Scholar
  2. [2]
    L. Davis, editor. Handbook of GA. Van Nostrand, Reinhold, NY, 1991.Google Scholar
  3. [3]
    K. DeJong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Phd’s thesis, University of Michigan, Ann Arbor, MI, 1975.Google Scholar
  4. [4]
    B.R. Fox and M.B. McMahon. Genetic Operators for Sequencing Problems. In Rawlins [12], pages 284–300.Google Scholar
  5. [5]
    A. Geist, A. Beguellin, J. Dongarra, et al. PVM: Parallel Virtual Machine. A User’s Guide and Tutorial for Net. Parallel Comp. The MIT Press, 1994.Google Scholar
  6. [6]
    M. Gorges-Schleuter. Explicit Parallelism of Genetic Algorithms through Population Structures. Parallel Problem Solving from Nature, pages 150–159, 1991.Google Scholar
  7. [7]
    J. Holland. A daptation in Natural and Artificial Systems. U. Mich. Press, 1975.Google Scholar
  8. [8]
    Z. Michalewicz. Genetic Algorithms+Data Structures=Evolutionary Programs. Springer-Verlag, Berlin, Germany, 1993.Google Scholar
  9. [9]
    T.M. Mitchell. Machine Learning. Series in CS. McGraw-Hill, 1997.Google Scholar
  10. [10]
    H. Mühlenbein. Evolution in Time and Space—The Parallel Genetic Algorithm. In Rawlins [12], pages 317–337.Google Scholar
  11. [11]
    S. Rana, A.E. Howe, D. Whitley, and K. Mathias. Comparing Heuristic, Evolutionary and Local Search Approaches to Scheduling. In Proc. of the 3 rd Artificial Intelligence Planning Systems Conference, 1996.Google Scholar
  12. [12]
    G.J.E. Rawlins, editor. Foundations of Genetic Algorithms. Morgan-Kaufmann Publishers, Inc., San Mateo, California, 1991.MATHGoogle Scholar
  13. [13]
    T. Starkweather, D. Whitley, and K. Mathias. Optimization Using Distributed Genetic Algorithms. Parallel Problem Solving from Nature, 1991.Google Scholar
  14. [14]
    R. Tanese. Distributed Genetic Algorithms. In Proc. of the 3 rd Int. Conf. on Genetic Algorithms, pages 434–439. Morgan-Kaufmann Publishers, Inc., 1989.Google Scholar
  15. [15]
    D. Whitley. A Genetic Algorithm Tutorial. Stat. and Computing, 4:65–85, 1994.Google Scholar
  16. [16]
    D. Whitley and T. Starkweather. Genitor II: a Distributed Genetic Algorithm. Journal Expt. Theory Artificial Intelligence, 2:189–214, 1990.Google Scholar
  17. [17]
    D. Whitley, T. Starkweather, and D. Shaner. The Traveling Salesman and Sequence Scheduling: Quality Solutions Using Genetic Edge Recombination. In Davis [2], chapter 22, pages 350–372.Google Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • Giuseppe Sena
    • 1
  • Germinal Isern
    • 1
  • Dalila Megherbi
    • 2
  1. 1.College of Computer ScienceNortheastern UniversityBostonUSA
  2. 2.Division of EngineeringUniversity of DenverDenverUSA
  3. 3.Technology Corp.CambridgeUSA
  4. 4.Universidad Central de VenezuelaCaracasVenezuela

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