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Chapitre D'ouvrage Année : 2019

Unsupervised Learning Bee Swarm Optimization Metaheuristic

Résumé

In this work, we investigate the use of unsupervised data mining techniques to speed up Bee Swarm Optimization metaheuristic (BSO). Knowledge is extracted dynamically during the search process in order to reduce the number of candidate solutions to be evaluated. One approach uses clustering (for grouping similar solutions) and evaluates only clusters centers considered as representatives. The second uses Frequent itemset mining for guiding the search process to promising solutions. The proposed hybrid algorithms are tested on MaxSAT instances and results show that a significant reduction in time execution can be obtained for large instances while maintaining equivalent quality compared to the original BSO.
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Dates et versions

hal-03251454 , version 1 (07-06-2021)

Identifiants

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Souhila Sadeg, Leila Hamdad, Mouloud Haouas, Kouider Abderrahmane, Karima Benatchba, et al.. Unsupervised Learning Bee Swarm Optimization Metaheuristic. International Work-Conference on Artificial Neural Networks, 11507, pp.773-784, 2019, 978-3-030-20517-1. ⟨10.1007/978-3-030-20518-8_64⟩. ⟨hal-03251454⟩
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