A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics

García, José; Crawford, Broderick; Soto, Ricardo; Astorga, Gino

doi:10.1016/j.swevo.2018.08.006

Cited by 56 publications

(30 citation statements)

References 65 publications

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“…The comparison and its results are described in Section 5.3. The design of the binarization framework using k-means is found in [50]. Finally, the third group of experiments developed the comparison between db-scan and TF, which is detailed in section 5.4.…”

Section: Resultsmentioning

confidence: 99%

“…The TF takes values of R n and generates transition probability values in [0, 1] n . Depending on the form of the function, they are generally classified as S-shaped [49] and V-shaped functions [50]. However, in recent years, the study of transfer functions has been extended by defining new families.…”

Section: Related Binarization Workmentioning

confidence: 99%

“…These measurements are defined in Equations (9)-(12) and through the use of radar plots determine which is the appropriate parameterization. More detail on the methodology used can be found in [29,50].…”

Section: Parameter Settingsmentioning

confidence: 99%

“…The k-means technique has been used to obtain binary versions of swarm intelligence algorithms. This technique has been successfully applied to the set-covering problem [50] and to the knapsack problem [29]. Unlike db-scan, k-means needs to set the number of clusters; therefore, this number is a parameter to estimate.…”

Section: K-means Algorithm Comparisonmentioning

confidence: 99%

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A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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Section: Resultsmentioning

confidence: 99%

Section: Related Binarization Workmentioning

confidence: 99%

Section: Parameter Settingsmentioning

confidence: 99%

Section: K-means Algorithm Comparisonmentioning

confidence: 99%

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A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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“…From the research point of view, these problems present interesting challenges in the areas of operations research, computational complexity, and algorithm theory. Examples of combinatorial problems are found in, scheduling problems [1,2], transport [2], machine learning [3], facility layout design [4], logistics [5], allocation resources [6,7], routing problems [8,9], robotics applications [10], civil engineering problem [11][12][13], engineering design problem [14], fault diagnosis of machinery [15], and social sustainability of infrastructure projects [16], among others. Combinatorial optimization algorithms should explore the solutions space to find optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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A Binary Sine-Cosine Algorithm Applied to the Knapsack Problem

Pinto

Peña

Valenzuela

et al. 2019

Advances in Intelligent Systems and Computing

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A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics

Cited by 56 publications

References 65 publications

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

A Binary Sine-Cosine Algorithm Applied to the Knapsack Problem

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