Abstract
Migrating Birds Optimization (MBO) algorithm is a promising metaheuristic algorithm recently introduced to the optimization community. Despite its superior performance, one drawback of MBO is its occasional aggressive movement to better solutions while searching the solution space. On the other hand, simulated annealing is a well-established metaheuristic optimization method with a search strategy that is particularly designed to avoid getting stuck at local optima. In this study, we present hybridization of the MBO algorithm with the SA algorithm by embedding the exploration strategy of SA into the MBO, which we call Hybrid MBO. In order to investigate impact of this hybridization, we test Hybrid MBO on 100 Quadratic Assignment Problem (QAP) instances taken from the QAPLIB. Our results show that Hybrid MBO algorithm outperforms MBO in about two-thirds of all the test instances, indicating a significant increase in performance.
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Algin, R., Alkaya, A.F., Aksakalli, V. (2020). Hybridization of Migrating Birds Optimization with Simulated Annealing. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_19
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DOI: https://doi.org/10.1007/978-3-030-14347-3_19
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