Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation

The Artificial Hummingbird Algorithm (AHA) is a metaheuristic optimization technique inspired by the behaviours and foraging strategies of hummingbirds. Known for their extraordinary agility and accuracy in collecting nectar, hummingbirds provide an exemplary framework for tackling complex optimizat...

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Vydané v:Computer science review Ročník 56; s. 100727
Hlavní autori: Sasmal, Buddhadev, Das, Arunita, Dhal, Krishna Gopal, Saha, Ramesh, Rai, Rebika, Bharasa, Totan, Houssein, Essam H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.05.2025
Predmet:
Artificial hummingbird algorithm
Metaheuristic algorithm
Nature-inspired optimization algorithm
Optimization
Swarm intelligence
Swarm intelligence
Artificial hummingbird algorithm
Metaheuristic algorithm
Optimization
Nature-inspired optimization algorithm
ISSN:1574-0137
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Abstract The Artificial Hummingbird Algorithm (AHA) is a metaheuristic optimization technique inspired by the behaviours and foraging strategies of hummingbirds. Known for their extraordinary agility and accuracy in collecting nectar, hummingbirds provide an exemplary framework for tackling complex optimization problems. Developed by Zhao et al. in 2022, AHA has swiftly attracted interest within the research community because to its exceptional performance and adaptability. This study provides a detailed and comprehensive review of AHA, exploring the diverse versions and modifications published in multiple research papers since its inception in 2022, with 23 % appearing in international conference papers and 75 % in esteemed peer-reviewed journals. The variants of AHA covered in this paper include 55 % of classical AHA, 17 % of improved AHA, 11 % of hybridization, 2 % of binary, 15 % of multi-objective variants, respectively. Furthermore, the applications of AHA illustrate its effectiveness and adaptability across various fields, with 42 % in power and control engineering, 11 % in optimizing deep learning models, 10 % in engineering design challenges, and 8 % in renewable energy sources. The algorithm has been utilized substantially in the domain of IoT, wireless sensor networks, wind energy, and fog computing. Furthermore, we also evaluate the performance of the AHA in the image clustering domain, and the findings revealed that the AHA performs better in comparison to the other tested methods. The main objectives of this study are to deliver a comprehensive review of AHA, emphasizing its novel methodology, and analyzing its various variants and their applications in numerous fields. As nature-inspired optimization methods continue to evolve, this survey paper expected to serves as a valuable resource for researchers aiming to gain a comprehensive understanding of AHA, its progression, and its diverse applications in solving complex optimization problems.
AbstractList The Artificial Hummingbird Algorithm (AHA) is a metaheuristic optimization technique inspired by the behaviours and foraging strategies of hummingbirds. Known for their extraordinary agility and accuracy in collecting nectar, hummingbirds provide an exemplary framework for tackling complex optimization problems. Developed by Zhao et al. in 2022, AHA has swiftly attracted interest within the research community because to its exceptional performance and adaptability. This study provides a detailed and comprehensive review of AHA, exploring the diverse versions and modifications published in multiple research papers since its inception in 2022, with 23 % appearing in international conference papers and 75 % in esteemed peer-reviewed journals. The variants of AHA covered in this paper include 55 % of classical AHA, 17 % of improved AHA, 11 % of hybridization, 2 % of binary, 15 % of multi-objective variants, respectively. Furthermore, the applications of AHA illustrate its effectiveness and adaptability across various fields, with 42 % in power and control engineering, 11 % in optimizing deep learning models, 10 % in engineering design challenges, and 8 % in renewable energy sources. The algorithm has been utilized substantially in the domain of IoT, wireless sensor networks, wind energy, and fog computing. Furthermore, we also evaluate the performance of the AHA in the image clustering domain, and the findings revealed that the AHA performs better in comparison to the other tested methods. The main objectives of this study are to deliver a comprehensive review of AHA, emphasizing its novel methodology, and analyzing its various variants and their applications in numerous fields. As nature-inspired optimization methods continue to evolve, this survey paper expected to serves as a valuable resource for researchers aiming to gain a comprehensive understanding of AHA, its progression, and its diverse applications in solving complex optimization problems.
ArticleNumber 100727
Author Saha, Ramesh
Das, Arunita
Dhal, Krishna Gopal
Rai, Rebika
Houssein, Essam H.
Sasmal, Buddhadev
Bharasa, Totan
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  givenname: Buddhadev
  surname: Sasmal
  fullname: Sasmal, Buddhadev
  email: buddhadev.sasmal2529@gmail.com
  organization: Department of Pure and Applied Science, Midnapore City College, Paschim Medinipur, West Bengal, India
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  givenname: Arunita
  surname: Das
  fullname: Das, Arunita
  email: arunita.das@midnaporecollege.ac.in
  organization: Department of Computer Science, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India
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  givenname: Krishna Gopal
  surname: Dhal
  fullname: Dhal, Krishna Gopal
  email: krishnagopal.dhal@midnaporecollege.ac.in
  organization: Department of Computer Science, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India
– sequence: 4
  givenname: Ramesh
  surname: Saha
  fullname: Saha, Ramesh
  email: ramesh1saha@gmail.com
  organization: Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT) Sonepat, Sonipat, Haryana, India
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  givenname: Rebika
  surname: Rai
  fullname: Rai, Rebika
  email: rrai@cus.ac.in
  organization: Department of Computer Applications, Sikkim University, Sikkim, India
– sequence: 6
  givenname: Totan
  surname: Bharasa
  fullname: Bharasa, Totan
  email: totanbharasa@beldacollege.ac.in
  organization: Department of Natural Science, Belda College, Paschim Medinipur, West Bengal, India
– sequence: 7
  givenname: Essam H.
  surname: Houssein
  fullname: Houssein, Essam H.
  email: essam.halim@mu.edu.eg
  organization: Faculty of Computers and Information, Minia University, Minia, Egypt
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CitedBy_id crossref_primary_10_1016_j_jece_2025_118339
crossref_primary_10_1038_s41598_025_02701_9
crossref_primary_10_1016_j_energy_2025_135807
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Keywords Swarm intelligence
Artificial hummingbird algorithm
Metaheuristic algorithm
Optimization
Nature-inspired optimization algorithm
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Snippet The Artificial Hummingbird Algorithm (AHA) is a metaheuristic optimization technique inspired by the behaviours and foraging strategies of hummingbirds. Known...
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SubjectTerms Artificial hummingbird algorithm
Metaheuristic algorithm
Nature-inspired optimization algorithm
Optimization
Swarm intelligence
Title Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation
URI https://dx.doi.org/10.1016/j.cosrev.2025.100727
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