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Flower Pollination Inspired Algorithm on Exchange Rates Prediction Case

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 3 (2023) · pp. 249–258

DOI
10.22146/ijccs.84223
ISSN
2460-7258, 1978-1520
Published
2023-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

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

Keywords

Collaboration network of the authors

17 authors · 30 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Udayana University
  • Islamic University of Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dzikri Rahadian FudholiUniversitas Gadjah Mada2
Chandra Kusuma DewaIslamic University of Indonesia1
Amanda Lailatul FadhilahIslamic University of Indonesia1
Reza PulunganUniversitas Gadjah Mada1
Muhammad IhsanAffiliation unknown1
Guntur Budi HerwantoUniversitas Gadjah Mada1
Annisa Maulida NingtyasUniversitas Gadjah Mada1
I Gede MujiyatnaUniversitas Gadjah Mada1
Kurniawan Eka NugrahaUniversitas Gadjah Mada1
Novia Arum SariUniversitas Gadjah Mada1
Paulus Josianlie PUniversitas Gadjah Mada1
M Ridho FuadinUniversitas Gadjah Mada1
Wayan Oger VihikanUdayana University1
I Made Wiraharja Jaya PutraUdayana University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Flower Pollination Inspired Algorithm on Exchange Rates Prediction Case</dc:title>
	<dc:creator>Trisna, I Nyoman Prayana</dc:creator>
	<dc:creator>Afiahayati, Afiahayati</dc:creator>
	<dc:creator>Auzan, Muhammad</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science; Artificial Intelligence; Computational Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">flower pollination algorithm; genetic algorithm; linear regression; exchange rate; global pollination; local pollination</dc:subject>
	<dc:description xml:lang="en-US">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</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/84223</dc:identifier>
	<dc:identifier>10.22146/ijccs.84223</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 3 (2023): July; 249-258</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/84223/36983</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>