TY - JOUR
T1 - Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics
AU - Cisternas-Caneo, Felipe
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Giachetti, Giovanni
AU - Paz, Álex
AU - Peña Fritz, Alvaro
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - binarization schemes
KW - chaotic maps
KW - Grey Wolf Optimizer
KW - knapsack problem
KW - Sine Cosine Algorithm
KW - Whale Optimization Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85183139676&partnerID=8YFLogxK
U2 - 10.3390/math12020262
DO - 10.3390/math12020262
M3 - Article
AN - SCOPUS:85183139676
SN - 2227-7390
VL - 12
JO - Mathematics
JF - Mathematics
IS - 2
M1 - 262
ER -