Implementation of a Parallel Genetic Algorithm on a Cluster of Workstations: The Travelling Salesman Problem, a Case Study

A parallel version of a Genetic Algorithm is presented and implemented on a cluster of workstations. Even though 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 show how the performance of the algorithm is a ected by variations on the number of slaves, population size, mutation rate, and mutation interval. The results presented show the utility, eÆciency and potential value of the proposed algorithm to tackle similar NP-Complete problems.