Clustering-Based Binarization Methods Applied to the Crow Search Algorithm for 0/1 Combinatorial Problems

Sergio Valdivia, Ricardo Soto, Broderick Crawford, Nicolás Caselli, Fernando Paredes, Carlos Castro and Rodrigo Olivares
Additional contact information
Sergio Valdivia: Dirección de Tecnologías de Información y Comunicación, Universidad de Valparaíso, 2361864 Valparaíso, Chile
Ricardo Soto: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
Broderick Crawford: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
Nicolás Caselli: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
Fernando Paredes: Escuela de Ingeniería Industrial, Universidad Diego Portales, 8370109 Santiago, Chile
Carlos Castro: Departamento de Informática, Universidad Técnica Federico Santa Maria, 2390123 Valparaíso, Chile
Rodrigo Olivares: Escuela de Ingeniería Informática, Universidad de Valparaíso, 2362905 Valparaíso, Chile

Mathematics, 2020, vol. 8, issue 7, 1-42

Abstract: Metaheuristics are smart problem solvers devoted to tackling particularly large optimization problems. During the last 20 years, they have largely been used to solve different problems from the academic as well as from the real-world. However, most of them have originally been designed for operating over real domain variables, being necessary to tailor its internal core, for instance, to be effective in a binary space of solutions. Various works have demonstrated that this internal modification, known as binarization, is not a simple task, since the several existing binarization ways may lead to very different results. This of course forces the user to implement and analyze a large list of binarization schemas for reaching good results. In this paper, we explore two efficient clustering methods, namely KMeans and DBscan to alter a metaheuristic in order to improve it, and thus do not require on the knowledge of an expert user for identifying which binarization strategy works better during the run. Both techniques have widely been applied to solve clustering problems, allowing us to exploit useful information gathered during the search to efficiently control and improve the binarization process. We integrate those techniques to a recent metaheuristic called Crow Search, and we conduct experiments where KMeans and DBscan are contrasted to 32 different binarization methods. The results show that the proposed approaches outperform most of the binarization strategies for a large list of well-known optimization instances.

Keywords: clustering techniques; crow search algorithm; binary domains; metaheuristics; bio-inspired computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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