Melola Discovery

← Melola Discovery

Jurnal SINTA 2

OPTIMUM MULTILEVEL THRESHOLDING HYBRID GA-PSO BY ALGORITHM

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 6 No. 1 (2013) · pp. 1–5

DOI
10.21609/jiki.v6i1.210
ISSN
2502-9274, 2088-7051
Published
2013-10-21
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm.

Keywords

Collaboration network of the authors

13 authors · 28 co-authorship links · 4 across different organisations

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

  • University of Brawijaya
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fitri UtaminingrumUniversity of Brawijaya2
Hurriyatul FitriyahUniversity of Brawijaya2
Rizal MaulanaUniversity of Brawijaya2
Randy Cahya WihandikaUniversity of Brawijaya2
Dahnial SyauqyUniversity of Brawijaya2
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Anny YuniartiInstitut Teknologi Sepuluh Nopember1
Tibyani TibyaniUniversity of Brawijaya1
M. Rizzo IrfanUniversity of Brawijaya1
Nurul Dyah MentariUniversity of Brawijaya1

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://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">OPTIMUM MULTILEVEL THRESHOLDING HYBRID GA-PSO BY ALGORITHM</dc:title>
	<dc:creator>hidayat, dwi taufik</dc:creator>
	<dc:creator>., Isnan</dc:creator>
	<dc:creator>Fauzi, Muhammad Ali</dc:creator>
	<dc:subject xml:lang="en-US">multilevel thresholding</dc:subject>
	<dc:subject xml:lang="en-US">image segmentation</dc:subject>
	<dc:subject xml:lang="en-US">histogram</dc:subject>
	<dc:subject xml:lang="en-US">genetic algorithm</dc:subject>
	<dc:subject xml:lang="en-US">particle swarm optimization</dc:subject>
	<dc:subject xml:lang="en-US">GA-PSO</dc:subject>
	<dc:subject xml:lang="en-US">otsu function</dc:subject>
	<dc:description xml:lang="en-US">The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2013-10-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/210</dc:identifier>
	<dc:identifier>10.21609/jiki.v6i1.210</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 6 No. 1 (2013): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 1-5</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v6i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/210/295</dc:relation>
</oai_dc:dc>