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

COMPUTER SCIENCE REVIEW 2025年 56卷

作者： Sasmal, Buddhadev Das, Arunita Dhal, Krishna Gopal Saha, Ramesh Rai, Rebika Bharasa, Totan Houssein, Essam H. Midnapore City Coll Dept Pure & Appl Sci Paschim Medinipur W Bengal India Midnapore Coll Autonomous Dept Comp Sci Paschim Medinipur W Bengal India Indian Inst Informat Technol IIIT Sonepat Dept Comp Sci & Engn Sonipat Haryana India Sikkim Univ Dept Comp Applicat Gangtok Sikkim India Belda Coll Dept Nat Sci Paschim Medinipur W Bengal India Minia Univ Fac Comp & Informat Al Minya Egypt

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.

关键词： artificial hummingbird algorithm Nature-inspired optimization algorithm Metaheuristic algorithm Optimization Swarm intelligence

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Damage detection and location using a simulated annealing-artificial hummingbird algorithm with an improved objective function

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年第1期24卷 129-147页

作者： Chen, Zhen Wang, Yikai Zhang, Kun Chan, Tommy H. T. Wang, Zhihao North China Univ Water Resources & Elect Power Sch Civil Engn & Commun Zhengzhou Peoples R China Queensland Univ Technol Sch Civil & Environm Engn Brisbane Qld Australia Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha Peoples R China North China Univ Water Resources & Elect Power Sch Civil Engn & Commun Zhengzhou 450045 Henan Peoples R China

Swarm intelligence algorithms and finite element model update technology are important issues in the field of structural damage detection. However, the complexity of engineering structural models normally leads to low computational efficiency and large detection errors in structural damage detection. To solve these problems, a simulated annealing-artificial hummingbird algorithm (SA-AHA) is proposed based on the artificial hummingbird algorithm (AHA). The Sobol sequence is used to improve the identification efficiency by optimizing the initial population distribution of the AHA. Then, the simulated annealing strategy is introduced to improve the detection accuracy by enhancing the global search ability of the AHA. In addition, a novel objective function is presented by combining modal flexibility residual, natural frequency residual, and trace sparse constraint of the structural model. Numerical simulations of a simply supported beam and a two-story rigid frame are carried out to verify the superiority of the proposed SA-AHA and the objective function. Simulation results demonstrate that the SA-AHA is better than the AHA in terms of damage computational efficiency and damage identification accuracy. Moreover, the new objective function can be more excellently applied to the SA-AHA than the previous one, which can be effectively used to locate and estimate the damage of the proposed SA-AHA in structure. Finally, experimental studies are carried out to verify the proposed method.

关键词： Structural damage detection artificial hummingbird algorithm simulated annealing-artificial hummingbird algorithm Sobol sequence modal flexibility trace sparse

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Optimized placement of distributed generators, capacitors, and EV charging stations in reconfigured radial distribution networks using enhanced artificial hummingbird algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Sahay, Sumeet Biswal, Saubhagya Ranjan Shankar, Gauri Jha, Amitkumar V. Appasani, Bhargav Srinivasulu, Avireni Nsengiyumva, Philibert Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India Vignans Fdn Sci Technol & Res Guntur Andhra Pradesh India KIIT Deemed Be Univ Sch Elect Engn Bhubaneswar India Mohan Babu Univ Erstwhile Sree Vidyanikethan Engn Coll Sch Engn & Technol Tirupati India Univ Rwanda Coll Sci & Technol Dept Elect & Elect Engn Kigali Rwanda

This study presents an assessment of concurrently identifying the best location and size of distributed generators (DGs), shunt capacitors (SCs), and electric vehicle charging stations (EVCSs) in optimally reconfigured radial distribution networks (RDNs). A comprehensive literature review indicates that this multi-unit combination has the potential to enhance RDN performance significantly, but it remains an underexplored area of research. Therefore, further in-depth investigation is necessary to understand and fully maximize the benefits of this method. The optimal placement and sizing (OPS) of the mentioned multi-unit in RDNs is realized by employing a metaheuristic optimization technique subject to the fulfillment of a well-defined fuzzified-objective function comprising of line losses reduction, power factor improvement, voltage deviation reduction, and DG penetration limit. Employing the concept of centroid-based oppositional learning (COL), an improved version of the artificial hummingbird algorithm (AHA), named COLAHA, is proposed to decipher the adopted issue. The results achieved utilizing the offered approach are matched with those of the additional innovative algorithms such as the basic AHA, arithmetic optimization algorithm, genetic algorithm, and whale optimization algorithm. By evaluating it against several benchmark functions, the effectiveness of the proposed COLAHA is established. The performance of the aforementioned studied algorithms is further tested to find the OPS of DGs, SCs and EVCSs in the standard IEEE 69- and 118-bus RDNs. Results obtained conclude that the COLAHA has offered quick convergence and the best results over the others for all the studied combinations of the multi-unit model.

关键词： artificial hummingbird algorithm Centroid-based oppositional learning Distributed generation Electric vehicle charging station Optimal reconfiguration Shunt capacitor

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Oppositional chaotic artificial hummingbird algorithm on engineering design optimization

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FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND 2025年 11卷

作者： Bhattacharjee, Vidyasagar Roy, Provas Kumar Tejani, Ghanshyam G. Mousavirad, Seyed Jalaleddin Dr BC Roy Engn Coll Dept Mech Engn Durgapur India Kalyani Govt Engn Coll Dept Elect Engn Kalyani India Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Dent Coll & Hosp Dept Res Analyt Chennai India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Mid Sweden Univ Dept Comp & Elect Engn Sundsvall Sweden

This paper proposes an enhanced-search form of the newly designed artificial hummingbird algorithm (AHA), named oppositional chaotic artificial hummingbird algorithm. The proposed OCAHA methodology incorporates the oppositional learning (OBL) in the population-initialization and at the ending event of each iteration for a faster convergence, and the chaos-embedded sequences of Gauss/mouse map to replace the random sequences of the three population-updating iterative stages of AHA, viz. guided, territorial and migration foraging to employ more diverse population for more solutional accuracy. The effectiveness of the method has been evaluated in two phases. OCAHA, the four state of the art algorithms, namely, PSO, DE, GWO and WOA, their recently developed effective variants, namely, SLPSO, MTDE, SOGWO and EWOA, and the inspiring optimizer AHA have been implemented on the 29 unconstrained CEC 2017 benchmark functions in the first phase. In the second phase, OCAHA has been verified on 10 challenging engineering cases, and compared with the concerned leading performances. Comprehensive analysis of the simulated outcomes using various statistical metrics and of the convergence profiles demonstrates that, the optimization ability of OCAHA on CEC 2017 is superior to all the comparing algorithms except MTDE. For engineering cases, OCAHA provides better searching performance, solution precision, robustness and convergence rate than all competing designs, and, on average, it has lowered the computational cost by 57.5% and 22.63% in term of function evaluations and the fitness objective by 2.4% and 0.23% in comparison to AHA and the chaotic version CAHA, respectively.

关键词： optimization metaheuristic algorithms artificial hummingbird algorithm opposition-based learning rule chaotic maps engineering design optimization problem

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Fractional Order PID Controller Design for an AVR System Using the artificial hummingbird Optimizer algorithm

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第9期35卷 3919-3943页

作者： Bouguenna, Elouahab Ladaci, Samir Lekouaghet, Badis Merrouche, Walid Benghanem, Mohamed CDER Renewable Energy Dev Ctr Algiers Algeria Natl Polytech Sch Dept Elect Algiers Algeria Res Ctr Ind Technol CRTI Algiers Algeria Islamic Univ Madinah Fac Sci Dept Phys Madinah Saudi Arabia

Optimizing the fractional-order PID (FOPID) controller using metaheuristic algorithms has gained significant popularity across various engineering domains. This paper introduces a novel approach by employing the artificial hummingbird algorithm (AHA), an innovative optimization technique inspired by the unique flight and foraging behaviors of hummingbirds, to fine-tune the FOPID controller for the automatic voltage regulator (AVR) system in synchronous generators, a critical component in maintaining voltage stability. The proposed method is rigorously tested using MATLAB/Simulink simulations under challenging conditions, including nonsmoothed higher-order dynamics of the control plant, parameter variations, time delays, and nonlinearities. The effectiveness of the AHA-based FOPID control strategy on the AVR system is comprehensively evaluated through extensive tests and analyses, focusing on aspects such as transient response, robustness, stability, and trajectory tracking. Moreover, a comparative assessment against established optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimizer (GWO), and artificial bee colony (ABC) is conducted. The results demonstrate the superiority of the proposed AHA-based FOPID control strategy, which significantly increases convergence speed. This is evidenced by a 25% faster rise time and a 45.74% shorter settling time compared to the GA-FOPID controller, the closest in performance for these metrics. Additionally, the AHA-based FOPID controller achieves a 92% reduction in steady-state oscillations compared to the ABC-FOPID controller, the nearest competitor in this aspect. These improvements highlight the AHA-based FOPID controller's superior efficiency and rapid response in achieving optimal performance. Hence, the proposed method shows remarkable success in enhancing stability and robustness, making it highly suitable for the design of practical high-performance applications.

关键词： artificial hummingbird algorithm automatic voltage regulator fractional order PID controller metaheuristic algorithm optimization

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artificial hummingbird algorithm For Optimal Reconfiguration Of Electrical Distribution networks 22

Artificial Hummingbird Algorithm For Optimal Reconfiguration...

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IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

作者： Zahraoui, Younes Bouhanik, Anes Korotko, Tarmo Rosin, Argo Mekhilef, Saad Tallinn Univ Technol FinEst Ctr Smart Cities Tallinn Estonia Univ Biskra Elect Engn Dept BP 145 Biskra 07000 Algeria Tallinn Univ Technol Dept Elect Power Engn & Mechatron Tallinn Estonia Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia

ISBN: (纸本)9798350387032;9798350387025

This paper suggests using the artificial hummingbird algorithm (AHA), a recently developed optimization technique, to solve the problem of finding the optimal distribution network reconfiguration (ODNR) to minimize power losses while meeting equality and inequality constraints. The suggested algorithm has been successfully tested on a 69-bus standard radial distribution network. The achieved results demonstrate the clear effectiveness and superiority of the AHA algorithm in comparison to various techniques documented in current scholarly publications.

关键词： optimal reconfiguration electrical distribution networks artificial hummingbird algorithm optimization power loss minimization

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A modified artificial hummingbird algorithm tuning optimal process noise covariance and measurement noise covariance for the Kalman filter in the noise reduction analysis of steering angle sensors in small electric vehicle

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Measurement 2025年 255卷

作者： Nattapong Nernchad Khachane Buakham Patiphat Janthirat Methad Janchuen Unchittha Prasatsap Electrical Technology Program Faculty of Industrial Technology Uttaradit Rajabhat University Uttaradit Thailand School of Renewable Energy and Smart Grid Technology (SGtech) Naresuan University Phitsanulok 65000 Thailand

In real operational environments, it is often challenging to fully determine the statistical properties of noise signals due to uncertainties in the system model and the noise characteristics of sensors, which can result in deviations in the process noise covariance ( Q ) and measurement noise covariance ( R ) parameters of the Kalman filter (KF). This research aims to reduce the noise from an angle sensor installed on the front-wheel steering system of a small electric vehicle model by finding the optimal Q and R values of the KF using the modified artificial hummingbird algorithm (MAHA). The case study is divided into three cases: Case Study 1-the wheels are suspended off the ground, and left–right steering is controlled independently, Case Study 2-vehicle operation on a hard, flat surface with left–right independent steering control, and Case Study 3-vehicle operation on a rough grass surface with left–right independent steering control. The analysis begins by extracting the system parameters to determine the bounds for searching for the optimal KF solutions with the MAHA algorithm. A cubic spline function is then constructed to serve as an ideal baseline for the system in each case study and then is calculated into the fitness value of the MAHA algorithm. The research results demonstrate the MAHA algorithm effectively identifies the optimal Q and R values for each case study, significantly reducing noise. The algorithm demonstrated noise reduction efficiency as high as 96.63 %.

关键词： Electric vehicle Kalman filter artificial hummingbird algorithm Cubic spline Minimize noise

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Solution to the deterministic and stochastic Optimal Reactive Power Dispatch by integration of solar, wind-hydro powers using Modified artificial hummingbird algorithm

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ENERGY REPORTS 2023年 9卷 4157-4173页

作者： Jamal, Raheela Zhang, Junzhe Men, Baohui Khan, Noor Habib Ebeed, Mohamed Kamel, Salah North China Elect Power Univ Sch Water Resources & Hydropower Engn Beijing 102206 Peoples R China Beijing Municipal Engn Consulting Co Ltd Beijing 100124 Peoples R China North China Elect Power Univ Dept New Energy State Key Lab Wind Power Beijing 102206 Peoples R China Sohag Univ Fac Engn Dept Elect Engn Sohag 82524 Egypt Univ Jaen Dept Elect Engn Jaen 23700 Spain Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt

In electrical power networks, the optimal reactive power dispatch (ORPD) problem is essential to the system studies to perform reliable and secure operations by maintaining the control variable within their permissible limits. An electric network consisting of thermal generators has been studied widely for optimal power dispatch problems. Increasing renewable energy resources (RERs) penetration into the electric power grid required power flow studies while integrating these resources. It is a strenuous task to incorporate renewable energy resources into the ORPD problem due to the stochastic nature of RERs. This paper solved the stochastic optimal reactive power dispatch (ORPD) problem considering the uncertainties of renewable resources such as;solar PV, wind turbine, and hydropower generation systems. The time-varying load demand and the power generated from the renewable energy resources are represented using the normal, the lognormal, the Weibull, and the Gumbel probability density function (PDFs). Then, the Monte Carlo simulations reduction of scenarios-based technique is applied to generate a suitable number of scenarios. The second contribution presents an efficient version of the artificial hummingbird algorithm (MAHA) for solving the Stochastic and Non-Stochastic ORPD. The proposed MAHA is based on the levy flight motion and the distance bandwidth motion around the best solution to enhance the exploration and exploitation behavior of the traditional AHA to avoid trapping into the local minima. The proposed algorithm is validated and tested on the IEEE 30-bus system for active power loss reduction, voltage profile improvement, and voltage stability enhancement. The results showed the effectiveness and superiority of the proposed MAHA for solving the ORPD problem compared to the well-known conventional algorithms such as AHA, GWO, SCA, DO, BWO and other state-of-the-art algorithms.(c) 2023 Published by Elsevier Ltd. This is an open access article under the CC

关键词： artificial hummingbird algorithm Optimal Reactive Power Dispatch Photovoltaic power Renewable energy resources Scenario -based approach

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Optimal Planning of Multiple Renewable Energy-Integrated Distribution System With Uncertainties Using artificial hummingbird algorithm

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IEEE ACCESS 2022年 10卷 40716-40730页

作者： Abid, Md Shadman Apon, Hasan Jamil Morshed, Khandaker Adil Ahmed, Ashik Islamic Univ Technol Dept Elect & Elect Engn Gazipur 1704 Bangladesh

Appropriate installation of renewable energy-based distributed generation units (RDGs) is one of the most important challenges and current topics of interest in the optimal functioning of modern power networks. Due to the intermittent nature of renewable energy sources, optimal allocation and sizing of RDGs, particularly photovoltaic (PV) and wind turbine (WT), remains a critical task. Based on a new metaheuristic known as the artificial hummingbird algorithm (AHA), this paper provides a novel approach for addressing the problem of RDG planning optimization. Considering various operational constraints, the optimization problem is developed with multiple objectives including power loss reduction, voltage stability margin (VSM) enhancement, voltage deviation minimization, and yearly economic savings. Furthermore, using relevant probability distribution functions, the ambiguities related with the stochastic nature of PV and WT output powers are evaluated. The proposed algorithm was compared to two of the recent metaheuristics applied in this domain known as improved harris hawks and particle swarm optimization algorithm (HHO-PSO) and hybrid of phasor particle swarm and gravitational search algorithm (PPSOGSA). The IEEE 33-bus and 69-bus systems are assessed as the test systems in this study. According to the findings, AHA delivers superior solutions and enhances the techno-economic benefits of distribution systems in all the scenarios evaluated.

关键词： Renewable energy sources Optimization Distribution networks Voltage Uncertainty Planning Resource management Distributed generation optimal DG placement artificial hummingbird algorithm renewable energy voltage stability margin voltage stability distribution system planning wind turbine photovoltaic generation

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Many-objective artificial hummingbird algorithm: an effective many-objective algorithm for engineering design problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第4期11卷 16-39页

作者： Kalita, Kanak Jangir, Pradeep Pandya, Sundaram B. Cep, Robert Abualigah, Laith Migdady, Hazem Daoud, Mohammad Sh Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Mech Engn Avadi 600062 India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Dept Bioinformat Chennai 602105 India Shri KJ Polytech Dept Elect Engn Bharuch 392001 India VSB Tech Univ Ostrava Fac Mech Engn Dept Machining Assembly & Engn Metrol Ostrava 70800 Czech Republic Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk 71491 Saudi Arabia Oman Coll Management & Technol CSMIS Dept Barka 320 Oman Al Ain Univ Coll Engn Abu Dhabi 112612 U Arab Emirates

Many-objective optimization presents unique challenges in balancing diversity and convergence of solutions. Traditional approaches struggle with this balance, leading to suboptimal solution distributions in the objective space especially at higher number of objectives. This necessitates the need for innovative strategies to adeptly manage these complexities. This study introduces a Many-Objective artificial hummingbird algorithm (MaOAHA), an advanced evolutionary algorithm designed to overcome the limitations of existing many-objective optimization methods. The objectives are to improve convergence rates, maintain solution diversity, and achieve a uniform distribution in the objective space. MaOAHA implements information feedback mechanism (IFM), reference point-based selection and association, non-dominated sorting, and niche preservation. The IFM utilizes historical data from previous generations to inform the update process, thereby improving the algorithm's the exploration and exploitation capabilities. Reference point-based selection, along with non-dominated sorting, ensures solutions are both close to the Pareto front and evenly spread in the objective space. Niche preservation and density estimation strategies are employed to maintain diversity and prevent overcrowding. The comprehensive experimental analysis benchmarks MaOAHA against four leading algorithms viz. Many-Objective Gradient-Based Optimizer, Many-Objective Particle Swarm Optimizer, Reference Vector Guided Evolutionary algorithm, and Nondominated Sorting Genetic algorithm III. The DTLZ1-DTLZ7 benchmark sets with four, six, and eight objectives and five real-world problems (RWMaOP1-RWMaOP5) are considered for performance assessment of the selected algorithms. The results demonstrate that internal parameter-free MaOAHA significantly outperforms its counterparts, achieving better generational distance by up to 52.38%, inverse generational distance by up to 38.09%, spacing by up to 56%, spread by up t

关键词： many-objective optimization multi-objective optimization diversity preservation artificial hummingbird algorithm non-dominated sorting

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