Clustering search

Oliveira, Alexandre César Muniz de; Chaves, Antônio Augusto; Lorena, Luiz Antônio Nogueira

doi:10.1590/s0101-74382013000100007

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…The Clustering Search (CS) [20] is a hybrid method which combines metaheuristic-based heuristics and local search heuristics. The search is intensified only in areas of the search space that deserve special attention (promising regions).…”

Section: Clustering Searchmentioning

confidence: 99%

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Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

Chaves

Lorena

Senne

et al. 2016

Computers & Operations Research

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The minimization of tool switches problem (MTSP) seeks a sequence to process a set of jobs so that the number of tool switches required is minimized. The MTSP is well known to be NP-hard. This paper presents a new hybrid heuristic based on the Biased Random Key Genetic Algorithm (BRKGA) and the Clustering Search (CS). The main idea of CS is to identify promising regions of the search space by generating solutions with a metaheuristic, such as BRKGA, and clustering them to be further explored with local search heuristics. The distinctive feature of the proposed method is to simplify this clustering process. Computational results for the MTSP considering instances available in the literature are presented to demonstrate the efficacy of the CS with BRKGA.

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Section: Clustering Searchmentioning

confidence: 99%

“…This paper presents a new application of the hybrid method Clustering Search (CS) [20] to solve the MTSP. The CS detects promising areas of the search space using a metaheuristic that generates solutions to be clustered.…”

Section: Introductionmentioning

confidence: 99%

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

Chaves

Lorena

Senne

et al. 2016

Computers & Operations Research

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“…Formally, clusters are characterized by tuples

(ς_{i}, τ_{i}, β_{i})

(Oliveira et al., ), which are, respectively, the center solution, its volume, and an inefficiency threshold. The center solution

ς_{i}

represents cluster i , and is the best solution of that cluster (actually the only one stored).…”

Section: Clustering Search Heuristic For the Pfclpmentioning

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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“…These promising areas are discovered by dividing the search space into clusters, created by grouping the solutions provided by the base metaheuristic. Oliveira et al [9] give a recent survey of CS characteristics and applications.…”

Section: Rank Aggregationmentioning

confidence: 99%

“…In this work, we employ Clustering Search (CS) [9], [10], [11] algorithm using Simulated Annealing (SA) [12] as it base metaheuristic for solving the rank aggregation problem. CS will clusters the solutions found by SA, grouping them in promising regions to be exploited by a local search mechanism.…”

Section: Introductionmentioning

confidence: 99%

Clustering Search Applied to Rank Aggregation

Lorena

et al. 2014

2014 Brazilian Conference on Intelligent Systems

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Abstract-Several practical applications require joining various rankings into a consensus ranking. These applications include gathering the results of multiple queries in information retrieval, deciding the result of a poll involving multiple judges and joining the outputs from ranking classification algorithms. Finding the ranking that best represents a set of rankings is a NP-hard problem, but a good solution can be found by using metaheuristics. In this paper, we investigate the use of Clustering Search (CS) algorithm allied to Simulated Annealing (SA) for solving the rank aggregation problem. CS will clusters the solutions found by SA in order to find promising regions in the search space, that can be further exploited by a local search. Experimental results on benchmark data sets show the potential of this approach to find a consensus ranking, achieving similar or better solutions than those found by other popular rank aggregation strategies.

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Clustering search

Cited by 19 publications

References 24 publications

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

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

Clustering Search Applied to Rank Aggregation

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