Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics

Felipe Cisternas-Caneo, Broderick Crawford, Ricardo Soto, Giovanni Giachetti, Álex Paz, Alvaro Peña Fritz

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

Resumen

Chaotic maps are sources of randomness formed by a set of rules and chaotic variables. They have been incorporated into metaheuristics because they improve the balance of exploration and exploitation, and with this, they allow one to obtain better results. In the present work, chaotic maps are used to modify the behavior of the binarization rules that allow continuous metaheuristics to solve binary combinatorial optimization problems. In particular, seven different chaotic maps, three different binarization rules, and three continuous metaheuristics are used, which are the Sine Cosine Algorithm, Grey Wolf Optimizer, and Whale Optimization Algorithm. A classic combinatorial optimization problem is solved: the 0-1 Knapsack Problem. Experimental results indicate that chaotic maps have an impact on the binarization rule, leading to better results. Specifically, experiments incorporating the standard binarization rule and the complement binarization rule performed better than experiments incorporating the elitist binarization rule. The experiment with the best results was STD_TENT, which uses the standard binarization rule and the tent chaotic map.

Idioma originalInglés
Número de artículo262
PublicaciónMathematics
Volumen12
N.º2
DOI
EstadoPublicada - ene. 2024

Áreas temáticas de ASJC Scopus

  • Informática (miscelánea)
  • Matemáticas General
  • Ingeniería (miscelánea)

Huella

Profundice en los temas de investigación de 'Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics'. En conjunto forman una huella única.

Citar esto