Flower Pollination Inspired Algorithm on Exchange Rates Prediction Case


I Nyoman Prayana Trisna(1*), Afiahayati Afiahayati(2), Muhammad Auzan(3)

(1) Information Technology Study Program, Udayana University, Bali
(2) Dept. of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(3) Dept. of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author


The flower pollination algorithm is a bio-inspired system that adapts a similar process to a genetic algorithm that aims for optimization problems. In this research, we examine the utilization of the flower pollination algorithm in linear regression for currency exchange cases. Each solution represents the regression coefficients. The population size for the solutions and the switching probability between global pollination and local pollination is experimented with in this research. The result shows that the final solution is obtained using a larger population and higher switch probability. Furthermore, our research finds that the increasing population size leads to considerable running time, where the probability of global pollination just slightly increases the running time


flower pollination algorithm; genetic algorithm; linear regression; exchange rate; global pollination; local pollination

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DOI: https://doi.org/10.22146/ijccs.84223

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