Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization

IAES International Journal of Artificial Intelligence · Vol. 10 No. 4 (2021) · hlm. 910–918

DOI
10.11591/ijai.v10.i4.pp910-918
ISSN
2252-8938, 2089-4872
Terbit
2021-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.

Kata kunci

Jaringan kolaborasi para penulis

12 penulis · 28 tautan kepenulisan bersama · 12 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Institut Teknologi Sepuluh Nopember
  • University of Surabaya
  • Institut Seni Indonesia Surakarta
  • Universitas Nurul Jadid
  • Kementerian Pariwisata
  • Diponegoro University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Muhammad Riv’an ArifInstitut Teknologi Sepuluh Nopember1
Qonita Qurratu AiniInstitut Teknologi Sepuluh Nopember1
Theophil Henry SoegiantoUniversity of Surabaya1
Jasti OhannaUniversity of Surabaya1
Ricky Andrean Fernanda GunawanAfiliasi tidak diketahui1
Ayu Putri RizkiaAfiliasi tidak diketahui1
Alief GunawanDiponegoro University1
Bambang SunartoInstitut Seni Indonesia Surakarta1
Mukhlis Anton NugrohoInstitut Seni Indonesia Surakarta1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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">A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization</dc:title>
	<dc:creator>Farisi, Olief Ilmandira Ratu</dc:creator>
	<dc:creator>Setiyono, Budi</dc:creator>
	<dc:creator>Danandjojo, R. Imbang</dc:creator>
	<dc:subject xml:lang="en-US">Optimization Algorithm; Graph Theory</dc:subject>
	<dc:subject xml:lang="en-US">Ant colony optimization, Firefly algorithm, Multi-depot multiple traveling salesman problem</dc:subject>
	<dc:description xml:lang="en-US">This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-12-01</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://ijai.iaescore.com/index.php/IJAI/article/view/20832</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i4.pp910-918</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 4: December 2021; 910-918</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20832/13240</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>