A Hybrid GRASP with Data Mining for the Maximum Diversity Problem

Santos, Luís; Ribeiro, Marcos Henrique; Plastino, Alexandre; Martins, Simone L.

doi:10.1007/11546245_11

Cited by 29 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of works rely on the Apriori algorithm (to identify interesting subsolutions) [65][66][67][68] or on CBR [69]. Another important issue in PMs is the population diversity, since maintaining it may lead to better performances.…”

Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

View full text Add to dashboard Cite

This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

show abstract

Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

View full text Add to dashboard Cite

show abstract

“…They insert the found subroutines into the new individuals. In the work of Ribeiro et al [37,38,40], the authors present a GRASP hybridized with several frequent item set mining algorithms: the Direct Count and the Intersect algorithms in [38] and the FPMax* in [40], which are Apriori-like approaches. These algorithms are used to extract patterns that are promising only on elite solutions.…”

Section: Datamining and Population Managementmentioning

confidence: 99%

Using Datamining Techniques to Help Metaheuristics: A Short Survey

Jourdan

Dhaenens

Talbi

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Hybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics and datamining through a short survey that enumerates the different opportunities of such combinations based on literature examples.

show abstract

“…The resulting method, the DM‐GRASP metaheuristic, achieved promising results not only in terms of solution Quality, but also in terms of execution time required to obtain good quality solutions. Later, the method was evaluated on two other applications, namely, the maximum diversity problem [16] and the server replication for reliable multicast problem [17], and the results were equally successful.…”

Section: Introductionmentioning

confidence: 99%

A hybrid data mining metaheuristic for the p‐median problem

Plastino

Fuchshuber

Martins

et al. 2011

Statistical Analysis

View full text Add to dashboard Cite

Metaheuristics 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 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 sub-optimal 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 sub-optimal solutions for the pmedian 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 sub-optimal 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.

show abstract

A Hybrid GRASP with Data Mining for the Maximum Diversity Problem

Cited by 29 publications

References 10 publications

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Using Datamining Techniques to Help Metaheuristics: A Short Survey

A hybrid data mining metaheuristic for the p‐median problem

Contact Info

Product

Resources

About