Quaternionic Flower Pollination Algorithm
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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.
KeywordsFlower Pollination Algorithm Quaternions Metaheuristics
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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