A hybrid data mining GRASP with path-relinking

AbstractThe exploration of hybrid metaheuristics—combination of metaheuristics with concepts and processes from other research areas—has been an important trend in combinatorial optimization research. An instance of this study is the hybrid version of the GRASP metaheuristic that incorporates a data mining process. Traditional GRASP is an iterative metaheuristic which returns the best solution reached over all iterations. In the hybrid GRASP proposal, after executing a significant number of iterations, the data mining process extracts patterns from an elite set of sub-optimal solutions for the optimization problem. These patterns present characteristics of near optimal solutions and can be used to guide the following GRASP iterations in the search through the combinatorial solution space. The hybrid data mining GRASP has been successfully applied for different combinatorial problems: the set packing problem, the maximum diversity problem, the server replication for reliable multicast problem and the p-median problem. In this work, we show that, not only the traditional GRASP, but also GRASP improved with the path-relinking heuristic—a memory-based intensification strategy—could benefit from exploring a data mining procedure. Computational experiments, comparing traditional GRASP with path-relinking and different path-relinking hybrid proposals, showed that employing the combination of path-relinking and data mining made the GRASP find better results in less computational time. Another contribution of this work is the application of the path-relinking hybrid proposal for the 2-path network design problem, which improved the state-of-the-art solutions for this problem