Abstract
Honey Bees Mating Optimization (HBMO) is a novel developed method used in different engineering areas. Optimization process in this algorithm is inspired of natural mating behavior between bees. In this paper, we have attempted to create a reciprocal relation between learning and evolution which can produce an algorithm with the power of dominating local optimums and finding global optima. In the proposed model, a set of learning Automata, which can produce reinforcement signal by obtaining feedback from queens, is attributed to each drone. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.
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Azadehgan, V., Meybodi, M.R., Jafarian, N., Jafarieh, F. (2011). Discrete Binary Honey Bees Mating Optimization with Capability of Learning. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_108
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DOI: https://doi.org/10.1007/978-3-642-25734-6_108
Publisher Name: Springer, Berlin, Heidelberg
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