A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

• Published in: Mathematics (Basel) Vol. 8; no. 4; p. 507
• Main Authors: García, José, Moraga, Paola, Valenzuela, Matias, Pinto, Hernan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2020
• Subjects: Adaptation; Algorithms; Capital budgeting; combinatorial optimization; db-scan; Design; Fault diagnosis; Food science; Hybridization; knapsack; Knapsack problem; Machine learning; metaheuristics; Multidimensional methods; Operators (mathematics); Optimization; Swarm intelligence; Transfer functions
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math8040507

This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.