Abstract
Metaheuristic-based optimization techniques offer an elegant and easy-to-follow framework to optimize different types of problems, ranging from aerodynamics to machine learning. Though such techniques are suitable for global optimization, they can still be get trapped locally under certain conditions, thus leading to reduced performance. In this work, we propose a quaternionic-based Flower Pollination Algorithm (FPA), which extends standard FPA to possibly smoother search spaces based on hypercomplex representations. We show the proposed approach is more accurate than five other metaheuristic techniques in four benchmarking functions. We also present a parallel version of the proposed approach that runs much faster.
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Notes
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Notice that all parameter values used in this work have been empirically set.
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Acknowledgments
The authors would like to thank FAPESP grants #2014/16250-9, #2014/12236-1, #2015/25739-4 and #2016/21243-7, as well as Capes, CNPq grant #306166/2014-3, and Capes PROCAD 2966/2014.
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Rosa, G.H., Afonso, L.C.S., Baldassin, A., Papa, J.P., Yang, XS. (2017). Quaternionic Flower Pollination Algorithm. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_5
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DOI: https://doi.org/10.1007/978-3-319-64698-5_5
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