TY - GEN
T1 - Enhancing Reptile Search Algorithm Performance for the Knapsack Problem with Integration of Chaotic Map
AU - Barrera-García, José
AU - Cisternas-Caneo, Felipe
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Becerra-Rozas, Marcelo
AU - Giachetti, Giovanni
AU - Monfroy, Eric
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - This study investigates the binarization process of the Reptile Search Algorithm (RSA) using chaotic maps to solve the Knapsack Problem. We evaluate RSA, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) using the S4 transfer function with four binarization strategies: standard, standard with chaotic maps, elitist, and elitist with chaotic maps. Experimental results show that standard binarization strategies, particularly RSA with standard binarization rule (STD) and RSA with standard binarization rule with a chaotic map (STD_SINE), consistently outperform elitist strategies across various Knapsack problem instances. Including chaotic maps, especially the sine chaotic map, slightly improves performance. Convergence analysis reveals that standard binarization ensures steady and strong convergence, while elitist binarization accelerates convergence but may risk settling on local optima early. This research highlights the importance of selecting appropriate binarization strategies and suggests further exploration of chaotic maps to enhance the performance of metaheuristic algorithms in solving binary combinatorial optimization problems.
AB - This study investigates the binarization process of the Reptile Search Algorithm (RSA) using chaotic maps to solve the Knapsack Problem. We evaluate RSA, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) using the S4 transfer function with four binarization strategies: standard, standard with chaotic maps, elitist, and elitist with chaotic maps. Experimental results show that standard binarization strategies, particularly RSA with standard binarization rule (STD) and RSA with standard binarization rule with a chaotic map (STD_SINE), consistently outperform elitist strategies across various Knapsack problem instances. Including chaotic maps, especially the sine chaotic map, slightly improves performance. Convergence analysis reveals that standard binarization ensures steady and strong convergence, while elitist binarization accelerates convergence but may risk settling on local optima early. This research highlights the importance of selecting appropriate binarization strategies and suggests further exploration of chaotic maps to enhance the performance of metaheuristic algorithms in solving binary combinatorial optimization problems.
KW - Binarization Schemes
KW - Chaotic Maps
KW - Combinatorial Problems
KW - Metaheuristics
KW - Reptile Search Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85208039763&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-75543-9_6
DO - 10.1007/978-3-031-75543-9_6
M3 - Conference contribution
AN - SCOPUS:85208039763
SN - 9783031755422
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 70
EP - 81
BT - Advances in Soft Computing - 23rd Mexican International Conference on Artificial Intelligence, MICAI 2024, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Ochoa-Ruiz, Gilberto
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd Mexican International Conference on Artificial Intelligence, MICAI 2024
Y2 - 21 October 2024 through 25 October 2024
ER -