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

José García, Paola Moraga, Matias Valenzuela and Hernan Pinto
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José García: Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
Paola Moraga: Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
Matias Valenzuela: Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
Hernan Pinto: Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile

Mathematics, 2020, vol. 8, issue 4, 1-22

Abstract: 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.

Keywords: combinatorial optimization; machine learning; metaheuristics; db-scan; knapsack (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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