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)


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.


Genetic Algorithm Travel Salesman Problem Travel Salesman Problem Crossover Operator Hamiltonian Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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

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