An analysis of a KNN perturbation operator: An application to the binarization of continuous metaheuristics

José García, Gino Astorga, Víctor Yepes

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Article number225
Pages (from-to)1-20
Number of pages20
Issue number3
StatePublished - 1 Feb 2021


  • Combinatorial optimization
  • KNN
  • Machine learning
  • Metaheuristics
  • Transfer functions


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