Making a state-of-the-art heuristic faster with data mining

Making a state-of-the-art heuristic faster with data mining Hybrid metaheuristics—developed based on the combination of metaheuristics with concepts and techniques from other research areas—represent an important subject in combinatorial optimization research. Data mining techniques have been coupled with metaheuristics in order to obtain patterns of suboptimal solutions, which are used to guide the search for better-cost solutions. In this paper, we incorporate a data mining procedure into a state-of-the-art heuristic for a specific problem in order to give evidences that, when a technique is able to reach an optimal solution, or a near-optimal solution with little chance of improvements, the mined patterns could be used to guide the search for the optimal or near optimal solution in less computational time. We developed a data mining hybrid version of a previously proposed and state-of-the-art multistart heuristic for the classical $$p$$ p -median problem. Computational experiments, conducted on a set of instances from the literature, showed that the new version of the heuristic was able to reach optimal and near-optimal solutions, on average, 27.32 % faster than the original strategy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Operations Research Springer Journals

Making a state-of-the-art heuristic faster with data mining

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Publisher
Springer US
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Business and Management; Operations Research/Decision Theory; Combinatorics; Theory of Computation
ISSN
0254-5330
eISSN
1572-9338
D.O.I.
10.1007/s10479-014-1693-4
Publisher site
See Article on Publisher Site

Abstract

Hybrid metaheuristics—developed based on the combination of metaheuristics with concepts and techniques from other research areas—represent an important subject in combinatorial optimization research. Data mining techniques have been coupled with metaheuristics in order to obtain patterns of suboptimal solutions, which are used to guide the search for better-cost solutions. In this paper, we incorporate a data mining procedure into a state-of-the-art heuristic for a specific problem in order to give evidences that, when a technique is able to reach an optimal solution, or a near-optimal solution with little chance of improvements, the mined patterns could be used to guide the search for the optimal or near optimal solution in less computational time. We developed a data mining hybrid version of a previously proposed and state-of-the-art multistart heuristic for the classical $$p$$ p -median problem. Computational experiments, conducted on a set of instances from the literature, showed that the new version of the heuristic was able to reach optimal and near-optimal solutions, on average, 27.32 % faster than the original strategy.

Journal

Annals of Operations ResearchSpringer Journals

Published: Sep 4, 2014

References

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