Abstract
Metaheuristics are general high-level procedures that coordinate simple heuristics and rules to find good approximate solutions to computationally difficult combinatorial optimization problems. Parallel implementations of metaheuristics appear quite naturally as an effective approach to speedup the search for approximate solutions. Besides the accelerations obtained, parallelization also allows solving larger problems or finding better solutions. We present in this work four slightly differing strategies for the parallelization of an extended GRASP with ILS heuristic for the mirrored traveling tournament problem, with the objective of harnessing the benefits of grid computing. Computational experiments on a dedicated cluster illustrate the effectiveness and the scalability of the proposed strategies. In particular, we show that the parallel strategy implementing cooperation through a pool of elite solutions scales better than the others and is able to find solutions that cannot be reached by the others. Computational grids are distributed high latency environments which offer significantly more computing power than traditional clusters. The best parallel strategy was also implemented and tested using a true grid platform. We report original results from pioneer computational experiments on a shared computational grid formed by 82 machines distributed over four clusters in three cities, illustrating the potential of the application of computational grids in the fields of metaheuristics and combinatorial optimization.
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Araújo, A.P., Boeres, C., Rebello, V.E., Ribeiro, C.C., Urrutia, S. (2007). Exploring Grid Implementations of Parallel Cooperative Metaheuristics. In: Doerner, K.F., Gendreau, M., Greistorfer, P., Gutjahr, W., Hartl, R.F., Reimann, M. (eds) Metaheuristics. Operations Research/Computer Science Interfaces Series, vol 39. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-71921-4_16
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