A hybrid data mining metaheuristic for the p‐median problem

Abstract

AbstractMetaheuristics represent an important class of techniques to solve, approximately, hard combinatorial optimization problems for which the use of exact methods is impractical. In this work, we propose a hybrid version of the Greedy Randomized Adaptive Search Procedures (GRASP) metaheuristic, which incorporates a data mining process, to solve the p‐median problem. We believe that patterns obtained by a data mining technique, from a set of suboptimal solutions of a combinatorial optimization problem, can be used to guide metaheuristic procedures in the search for better solutions. 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 suboptimal solutions for the p‐median 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. Computational experiments, comparing traditional GRASP and different data mining hybrid proposals for the p‐median problem, showed that employing patterns mined from an elite set of suboptimal solutions made the hybrid GRASP find better results. Besides, the conducted experiments also evidenced that incorporating a data mining technique into a metaheuristic accelerated the process of finding near optimal and optimal solutions. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011