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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number262
Issue number2
Publication statusPublished - Jan 2024


  • binarization schemes
  • chaotic maps
  • Grey Wolf Optimizer
  • knapsack problem
  • Sine Cosine Algorithm
  • Whale Optimization Algorithm

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Mathematics
  • Engineering (miscellaneous)


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