A db-scan hybrid algorithm: An application to the multidimensional knapsack problem

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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.

Original languageEnglish
Article number507
Issue number4
StatePublished - 1 Apr 2020


  • Combinatorial optimization
  • Db-scan
  • Knapsack
  • Machine learning
  • Metaheuristics


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