Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation - Sasmal, Buddhadev; Das, Arunita; Dhal, Krishna Gopal; Saha, Ramesh; Rai, Rebika; Bharasa, Totan; Houssein, Essam H. (Computer science review, 2025)
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.