An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

José García, Gino Astorga and Víctor Yepes
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José García: Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Gino Astorga: Escuela de Negocios Internacionales, Universidad de Valparaíso, Valparaíso 2361864, Chile
Víctor Yepes: Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain

Mathematics, 2021, vol. 9, issue 3, 1-20

Abstract: The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.

Keywords: combinatorial optimization; machine learning; KNN; metaheuristics; transfer functions (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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