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

Int Trans Operational Res

Rosseti

2020

Self Cite

Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high‐quality solutions can lead to an efficient exploration of the search space, along with a significant reduction of computational time. In this paper, a GRASP‐based state‐of‐the‐art heuristic for the minimum latency problem is improved by means of data mining techniques. Computational experiments showed that the hybrid heuristic with data mining was able to match or improve the solution quality for a large number of instances, together with a substantial reduction of running time. Besides, 32 new best‐known solutions are introduced to the literature. To support our results, statistical significance tests, analyses over the impact of mined patterns, comparisons based on running time as stopping criterion, and time‐to‐target plots are provided.

Section: Computational Resultsmentioning

confidence: 99%

Improving a state‐of‐the‐art heuristic for the minimum latency problem with data mining

Santana

Int Trans Operational Res

Rosseti

2020

Self Cite

“…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%

“…All these previous approaches follow the same structure, embedding data mining techniques within a GRASP multistart context. Even though Martins et al (2018a) did not work with a GRASP-based heuristic, their heuristic was also a multistart approach combining a constructive, local search, and path relinking heuristics, very similar to the DM-GRASP metaheuristic structure. Thus, although too many efforts have been taken to improve multistart metaheuristics, none has been expended on how to use patterns to improve other metaheuristics such as CS and SA.…”

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).…”

mentioning

confidence: 99%

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

Guerine

Rosseti

Int Trans Operational Res

2019

Self Cite

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.

“…This manuscript version is made available under the CC BY-NC-ND 4.0 license. Guerine et al, 2016;Maia et al, 2018;Martins et al, 2018a). Data-mining-hybridized heuristics were able to find solutions of higher quality while spending less computational time when compared to their non-hybridized counterparts and other state-of-the-art heuristics.…”

mentioning

confidence: 99%

MineReduce: An approach based on data mining for problem size reduction

Maia

Computers & Operations Research

Penna

2020

Self Cite

Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times. . This manuscript version is made available under the CC BY-NC-ND 4.0 license. Guerine et al., 2016;Maia et al., 2018;Martins et al., 2018a). Data-mining-hybridized heuristics were able to find solutions of higher quality while spending less computational time when compared to their non-hybridized counterparts and other state-of-the-art heuristics. They applied patterns (found by data mining procedures) to guide the construction of initial solutions.The difficulty in solving a COP is due to the fact that its solution space grows exponentially with its size. Therefore, problem size reduction (PSR) -which consists of reducing the size of a problem instance, then solving the reduced instance and expanding it back -has been found to be a very useful strategy in this context, especially in the case of large-scale problems, since it can significantly reduce the search space of a COP (Gavish & Srikanth, 1986;Fischer & Merz, 2007;Delgadillo et al., 2016).Building upon these ideas, in this paper, we present an approach named MineReduce, which uses mined patterns to perform problem size reduction. Previous methods that also incorporate data mining techniques into metaheuristics, in general, use the mined patterns as a starting point for the construction of initial solutions (Martins et al., 2018b). MineReduce, on the other hand, performs a PSR procedure by removing or merging elements that are in a pattern, finding a solution to the reduced instance, and expanding the solution found, which will be used as the starting point for a local search on the original solution space.In order to validate the MineReduce approach, we have applied it to extend a previous and state-of-theart heuristic for the heterogeneous fleet vehicle routing problem (HFVRP), obtaining significantly better results in terms of both solution quality and computational time.So, the main contribution of this work is twofold: an approach based on the idea of using mi...