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Article Dans Une Revue Computers & Industrial Engineering Année : 2024

An incremental method-based machine learning approach for max-min knapsack with multiple scenarios

Juntao Zhao
  • Fonction : Auteur
Xiaochuan Luo
  • Fonction : Auteur

Résumé

In this paper, the max–min knapsack problem with multiple scenarios is addressed using an incremental method-based machine learning approach. The method is structured around three key phases: a learning phase, an exploitation phase, and an exploration phase. During the learning phase, the algorithm initiates by generating a sequential set of initial elite solutions through a Q-table scoring procedure. Moving on to the exploitation phase, the goal is to improve the quality of the current solution through an intensification-based tabu search, emphasizing iterative solution optimization. The exploration phase introduces controlled randomness to diversify exploration, revealing promising sub-spaces and facilitating the discovery of new solutions. This iterative process results in a series of new elite solutions, some of which are combined to highlight the method’s efficiency. To maintain diversity, a new fusion operator is introduced, where couples related to elite solutions are combined, forming an incremental subset of elite solutions capable of exploring new solution subspaces. The proposed method is evaluated using benchmark instances from the literature, and its results are compared to those provided by recently published algorithms. The computational analysis underscores the algorithm’s competitiveness in producing high-quality solutions, often surpassing previously published methods with superior solution bounds.
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Dates et versions

hal-04483224 , version 1 (29-02-2024)

Identifiants

Citer

Juntao Zhao, Mhand Hifi, Yulin Zhang, Xiaochuan Luo. An incremental method-based machine learning approach for max-min knapsack with multiple scenarios. Computers & Industrial Engineering, 2024, pp.109984. ⟨10.1016/j.cie.2024.109984⟩. ⟨hal-04483224⟩
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