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ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 7 No. 2 (2014) · pp. 76–82

DOI
10.21609/jiki.v7i2.260
ISSN
2502-9274, 2088-7051
Published
2014-08-21
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Ant Colony Optimization (ACO) is a nature-inspired optimization algorithm which is motivated by ants foraging behavior. Due to its favorable advantages, ACO has been widely used to solve several NP-hard problems, including edge detection. Since ACO initially distributes ants at random, it may cause imbalance ant distribution which later affects path discovery process. In this paper an adaptive ACO is proposed to optimize edge detection by adaptively distributing ant according to gradient analysis. Ants are adaptively distributed according to gradient ratio of each image regions. Region which has bigger gradient ratio, will have bigger number of ant distribution. Experiments are conducted using images from various datasets. Precision and recall are used to quantitatively evaluate performance of the proposed algorithm. Precision and recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show that the adaptive ACO outperforms standard ACO which randomly distributes ants.

Keywords

Collaboration network of the authors

22 authors · 48 co-authorship links · 18 across different organisations

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

  • Institut Teknologi Sepuluh Nopember
  • Institut Teknologi Adhi Tama Surabaya
  • State University of Malang
  • Universitas Negeri Surabaya
  • Politeknik Negeri Malang
  • University of Brawijaya
  • Universitas Amikom Yogyakarta
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Wahyu Nur HidayatPoliteknik Negeri Malang2
Rizal Setya PerdanaUniversity of Brawijaya1
Reddy AlexandroInstitut Teknologi Sepuluh Nopember1
Wahyu HidayatUniversitas Amikom Yogyakarta1
Slamet WibawantoState University of Malang1
Ahmad HamdanState University of Malang1
M. M. ZubaidillahUniversitas Negeri Surabaya1
Sri PoedjiastoetiUniversitas Negeri Surabaya1
Luky Agus HermantoInstitut Teknologi Adhi Tama Surabaya1
Rifki Indra PerwiraUniversitas Pembangunan Nasional Veteran Yogyakarta1
Heparia Arum Ndaru RamdhaniSebelas Maret University1
Dian Meididewi NurainiKhon Kaen University1
Azhar Ahmad SmaragdinaAffiliation unknown1
Muhammad Ferdian FirmansyahAffiliation unknown1
Ajeng Ramadhani Lailul Mas’udahAffiliation unknown1
Asna Isyarotul AkbarAffiliation unknown1
Rahajeng Kartika SariAffiliation unknown1
Achmad HamdanAffiliation unknown1
Hary SuswantoAffiliation unknown1

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 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION</dc:title>
	<dc:creator>Liantoni, Febri</dc:creator>
	<dc:creator>Kirana, Kartika Candra</dc:creator>
	<dc:creator>Muliawati, Tri Hadiah</dc:creator>
	<dc:subject xml:lang="en-US">gradient</dc:subject>
	<dc:subject xml:lang="en-US">adaptive</dc:subject>
	<dc:subject xml:lang="en-US">random ant distribution</dc:subject>
	<dc:subject xml:lang="en-US">ant colony optimization</dc:subject>
	<dc:description xml:lang="en-US">Ant Colony Optimization (ACO) is a nature-inspired optimization algorithm which is motivated by ants foraging behavior. Due to its favorable advantages, ACO has been widely used to solve several NP-hard problems, including edge detection. Since ACO initially distributes ants at random, it may cause imbalance ant distribution which later affects path discovery process. In this paper an adaptive ACO is proposed to optimize edge detection by adaptively distributing ant according to gradient analysis. Ants are adaptively distributed according to gradient ratio of each image regions. Region which has bigger gradient ratio, will have bigger number of ant distribution. Experiments are conducted using images from various datasets. Precision and recall are used to quantitatively evaluate performance of the proposed algorithm. Precision and recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show that the adaptive ACO outperforms standard ACO which randomly distributes ants.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2014-08-21</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/260</dc:identifier>
	<dc:identifier>10.21609/jiki.v7i2.260</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 7 No. 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 76-82</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v7i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/260/318</dc:relation>
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