Abstract
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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Sadeg, S., Hamdad, L., Haouas, M., Abderrahmane, K., Benatchba, K., Habbas, Z. (2019). Unsupervised Learning Bee Swarm Optimization Metaheuristic. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_64
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