Adaptive and multi-mining versions of the DM-GRASP hybrid metaheuristic

Plastino, Alexandre; Barbalho, Hugo; Santos, Luís; Fuchshuber, Richard; Martins, Simone L.

doi:10.1007/s10732-013-9231-0

Cited by 24 publications

(21 citation statements)

References 22 publications

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“…As future work, the goal is to implement a multimining version of the DM-GRASP/VND, running the data mining procedure more than once. This idea, successfully applied in other hybrid data mining strategies (Barbalho et al, 2013;Plastino et al, 2013), consists of executing the data mining method whenever the elite set becomes stable.…”

Section: Discussionmentioning

confidence: 99%

Extending the Hybridization of Metaheuristics with Data Mining to a Broader Domain

Guerine¹,

Rosseti²,

Plastino³

2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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The incorporation of data mining techniques into metaheuristics has been efficiently adopted to solve several optimization problems. Nevertheless, we observe in the literature that this hybridization has been limited to problems in which the solutions are characterized by sets of (unordered) elements. In this work, we develop a hybrid data mining metaheuristic to solve a problem for which solutions are defined by sequences of elements. This way, we extend the domain of combinatorial optimization problems which can benefit from the combination of data mining and metaheuristic. Computational experiments showed that the proposed approach improves the pure algorithm both in the average quality of the solution and in execution time.

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Section: Discussionmentioning

confidence: 99%

Extending the Hybridization of Metaheuristics with Data Mining to a Broader Domain

Guerine¹,

Rosseti²,

Plastino³

2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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“…The experiments performed by Plastino et al. () to solve the server replication for reliable multicast problem showed that the multi‐DM‐GRASP (MDM‐GRASP) strategy, which explores the gradual evolution of the elite set of solutions, obtained better results than DM‐GRASP in less computational times. Multiple and adaptive executions of the data mining process improved the results obtained by the DM‐GRASP heuristic.…”

Section: Computational Experimentsmentioning

confidence: 99%

A GRASP with path‐relinking and restarts heuristic for the prize‐collecting generalized minimum spanning tree problem

Marzo

Ribeiro

2019

Int Trans Operational Res

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Given a graph with its vertex set partitioned into a set of groups, nonnegative costs associated to its edges, and nonnegative prizes associated to its vertices, the prize‐collecting generalized minimum spanning tree problem consists in finding a subtree of this graph that spans exactly one vertex of each group and minimizes the sum of the costs of the edges of the tree less the prizes of the selected vertices. It is a generalization of the NP‐hard generalized minimum spanning tree optimization problem. We propose a GRASP (greedy randomized adaptive search procedure) heuristic for its approximate solution, incorporating path‐relinking for search intensification and a restart strategy for search diversification. The hybridization of the GRASP with path‐relinking and restarts heuristic with a data mining strategy that is applied along with the GRASP iterations, after the elite set is modified and becomes stable, contributes to making the heuristic more robust. The computational experiments show that the heuristic developed in this work found very good solutions for test problems with up to 439 vertices. All input data for the test instances and detailed numerical results are made available from Mendeley Data.

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“…The idea is to collect a set of high-quality solutions of the optimization problem (called the elite set), apply the mining step over this elite set, which extracts a subset of elements (patterns) that frequently occurs in the referred set, and finally use these patterns to guide the search. The DM-GRASP framework has been applied to improve heuristics for different optimization problems (Santos et al, 2008;Plastino et al, 2011Plastino et al, , 2014Barbalho et al, 2013;Guerine et al, 2016;Martins et al, 2018a).…”

Section: Improving the Clustering Search With Data Miningmentioning

confidence: 99%

“…The DM-GRASP framework has been applied to improve state-of-the-art heuristics for different optimization problems such as the two-path network design problem (Barbalho et al, 2013), p-median problem (Plastino et al, 2011;Martins et al, 2018a), the efficient server replication for reliable multicast problem (Plastino et al, 2014), and the maximum diversity problem (Santos et al, 2008). Besides, based on the hypothesis that mining more than once could explore the gradual evolution of the elite set and extract refined patterns, the concept of multidata mining GRASP (MDM-GRASP) was also investigated (Plastino et al, 2011;Barbalho et al, 2013;Guerine et al, 2016).…”

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confidence: 99%

A hybrid data mining heuristic to solve the point‐feature cartographic label placement problem

Guerine

Rosseti

Plastino

2019

Int Trans Operational Res

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A clean map visualization requires the fewest possible overlaps and depends on how labels are attached to point features. In this paper, we address the cartographic label placement variant problem whose objective is to label a set of points maximizing the number of conflict‐free points. Thus, we propose a hybrid data mining heuristic to solve the point‐feature cartographic label placement problem based on a clustering search (CS) heuristic, a state‐of‐the‐art method for this problem. Although several works have investigated the combination of data mining and multistart metaheuristics, this is the first time data mining has been used to improve CS and simulated annealing based heuristics. Computational experiments showed that the proposed hybrid heuristic was able to reach better cost solutions than the original strategy, with the same time effort. The proposed heuristic also could find almost all known optimal solutions and improved most of the best results for the set of large instances reported so far in the literature.

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Adaptive and multi-mining versions of the DM-GRASP hybrid metaheuristic

Cited by 24 publications

References 22 publications

Extending the Hybridization of Metaheuristics with Data Mining to a Broader Domain

Extending the Hybridization of Metaheuristics with Data Mining to a Broader Domain

A GRASP with path‐relinking and restarts heuristic for the prize‐collecting generalized minimum spanning tree problem

A hybrid data mining heuristic to solve the point‐feature cartographic label placement problem

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