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Many problems formerly considered intractable have been satisfactorily resolved using approximate optimization methods called metaheuristics. These methods use a non-deterministic approach that finds good solutions, despite not ensuring the determination of the overall optimum. The success of a metaheuristic is conditioned on its capacity of alternating properly between the exploration and exploitation of solution spaces. During the process of searching for better solutions, a metaheuristic can be guided to regions of promising solutions using the acquisition of information on the problem under study. In this study this is done through the use of reinforcement learning. The performance of a metaheuristic can also be improved using multiple search trajectories, which act competitively and/or cooperatively. This can be accomplished using parallel processing. Thus, in this paper we propose a hybrid parallel implementation for the GRASP metaheuristics and the genetic al gorithm, using reinforcement learning, applied to the symmetric traveling salesman problem. |
