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A hunger game search algorithm for economic load dispatch

IAES International Journal of Artificial Intelligence · Vol. 11 No. 2 (2022) · hlm. 632–640

DOI
10.11591/ijai.v11.i2.pp632-640
ISSN
2252-8938, 2089-4872
Terbit
2022-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This work proposes a new approach to solve the economic load dispatch (ELD) issue in power systems by metaheuristic algorithms inspired by natural life. The problem to be resolved is to optimize the power system network with various constraints by considering the cutting in the cost of the resulting in the transmission of the electric system. The method used in this study is the hunger games search (HGS). This method duplicates the hungerdriven activity and the animal's choice of behavior. The proposed method is to add the concept of starvation as a process structure. Adaptive weights based on the concept of hunger are designed and used to simulate the effects of hunger on each trace process. To get the performance of the proposed method, this research uses mathematical methods, particle swarm optimization (PSO), differential evolution (DE), giza pyramids construction (GPC), and sine tree-seed algorithm (STSA) as a comparison. This study uses 2 case studies. In case study 1, the proposed method has a 0.16% better cost of generation than the mathematical method. Comparison of the HGS method with the PSO method in the second case study, it was found that the HGS method was 0.018% better than the PSO. From the research, it was found that the HGS method was superior.

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  • Universitas Negeri Surabaya
  • Institut Teknologi Sepuluh Nopember
  • Taibah University
  • Al al-Bayt University
  • Universidad de Guadalajara
  • Nigerian Defence Academy
  • Al-Balqa Applied University
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Rekan penulisOrganisasiArtikel bersama
Nita KusumawatiUniversitas Negeri Surabaya8
Ayusta Lukita WardaniUniversitas Negeri Surabaya6
Aditya PrapancaUniversitas Negeri Surabaya5
Laith AbualigahAl al-Bayt University4
Diego A. OlivaUniversidad de Guadalajara4
Pirim SetiarsoUniversitas Negeri Surabaya4
Nafisatus ZakiyahUniversitas Negeri Surabaya4
Reza RahmadianUniversitas Negeri Surabaya4
Mahendra WidyartonoUniversitas Negeri Surabaya4
Unit Three KartiniUniversitas Negeri Surabaya4
Aliyu SaboNigerian Defence Academy3
Hisham A. ShehadehAl-Balqa Applied University3
Joko JokoUniversitas Negeri Surabaya3
Erina RahmadyantiUniversitas Negeri Surabaya3
Heri SuryoatmojoInstitut Teknologi Sepuluh Nopember2
Feby Agung PamujiInstitut Teknologi Sepuluh Nopember2
Abeer A. AljohaniTaibah University2
Ashabul KahfiUniversitas Negeri Surabaya2
Sinta Anjas CahyaniUniversitas Negeri Surabaya2
Fadlurachman Faizal FachrirakarsieUniversitas Negeri Surabaya2
Aditya Chandra HermawanUniversitas Negeri Surabaya2
Supari SupariUniversitas Negeri Surabaya2
Nur Vidia Laksmi B.Universitas Negeri Surabaya1
Muhammad Syahril MubarokUniversitas Airlangga1
Moh. Zaenal EffendiAfiliasi tidak diketahui1
Tian‐Hua LiuNational Taiwan University of Science and Technology1
Toufik MziliChouaib Doukkali University1
Indri Wasa EstiningtyasUniversitas Negeri Surabaya1
Nunik Tri RahayuUniversitas Negeri Surabaya1
Riska Nur SafitriUniversitas Negeri Surabaya1
Wiyli YustantiUniversitas Negeri Surabaya1
Farid BaskoroUniversitas Negeri Surabaya1
Yuni YamasariUniversitas Negeri Surabaya1
Miftahur RohmanUniversitas Negeri Surabaya1
Rina HarimurtiUniversitas Negeri Surabaya1
Dimas Ahmad Nur Kholis SuhermantoUniversitas Negeri Surabaya1
Abeer AljohaniTaibah University1

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) 
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	<dc:title xml:lang="en-US">A hunger game search algorithm for economic load dispatch</dc:title>
	<dc:creator>Aribowo, Widi</dc:creator>
	<dc:creator>Muslim, Supari</dc:creator>
	<dc:creator>Suprianto, Bambang</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Economic load dispatch; Hunger games search; Metaheuristic; Optimization</dc:subject>
	<dc:description xml:lang="en-US">This work proposes a new approach to solve the economic load dispatch (ELD) issue in power systems by metaheuristic algorithms inspired by natural life. The problem to be resolved is to optimize the power system network with various constraints by considering the cutting in the cost of the resulting in the transmission of the electric system. The method used in this study is the hunger games search (HGS). This method duplicates the hungerdriven activity and the animal's choice of behavior. The proposed method is to add the concept of starvation as a process structure. Adaptive weights based on the concept of hunger are designed and used to simulate the effects of hunger on each trace process. To get the performance of the proposed method, this research uses mathematical methods, particle swarm optimization (PSO), differential evolution (DE), giza pyramids construction (GPC), and sine tree-seed algorithm (STSA) as a comparison. This study uses 2 case studies. In case study 1, the proposed method has a 0.16% better cost of generation than the mathematical method. Comparison of the HGS method with the PSO method in the second case study, it was found that the HGS method was 0.018% better than the PSO. From the research, it was found that the HGS method was superior.</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>2022-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21497</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i2.pp632-640</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 2: June 2022; 632-640</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21497/13364</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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