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A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Kiymac, Evren Kaya, Yasin Adana Alparslan Turkes Sci & Technol Univ Dept Comp Engn Adana Turkiye

Cardiac arrhythmias indicate cardiovascular disease which is the leading cause of mortality worldwide, and can be detected by an electrocardiogram (ECG). Automated deep learning methods have been developed to overcome the disadvantages of manual interpretation by medical experts. The performance of the networks strongly depends on hyperparameter optimization (HPO), and this NP-hard problem is suitable for metaheuris-tic (MH) methods. In this study, a novel method is proposed for the HPO of a convolutional neural network (CNN) arrhythmia classifier using an MH algorithm. The approach utilizes our variant of an MH method, named the memory-enhanced artificial hummingbird algorithm, which has an additional memory unit that stores the evaluations of the solutions and reduces the computation time significantly. The study also proposes a novel fitness function that considers both the accuracy rate and the total number of parameters of each candidate network. Experiments were conducted on raw ECG samples from the MIT-BIH arrhythmia database. The proposed method was compared with five other MH methods and achieved equal or outperforming results, with classification accuracy reaching 98.87%. The proposed method yielded promising results in finding a high-performing solution with relatively lower complexity.

关键词： Arrhythmia classification ECG Deep learning CNN Hyperparameter optimization artificial hummingbird algorithm

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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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Ameliorated artificial hummingbird algorithm for coordinated wind-solar-thermal generation scheduling problem in multiobjective framework

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APPLIED ENERGY 2022年 326卷

作者： Kansal, Veenus Dhillon, J. S. St Longowal Inst Engn & Technol Longowal Elect & Instrumentat Engn Dept Sangrur India

This paper proposed an optimization technique, namely ameliorated artificial hummingbird algorithm (AAHA), that blends artificial hummingbird algorithm (AHA) with simplex search strategy (SSS) to solve the coordinated wind-solar-thermal generation scheduling problem. The AAHA simulates the foraging behaviour of humming-birds for food, including guided, territorial, and migration foraging. Guided foraging helps in the higher exploration in the initial stages, and territorial foraging performs the exploitation in its neighbourhood. Migration foraging explores the search space. The SSS enhances the weak territorial and migration foraging of AHA by improving the exploitation mechanism. The proposed method is simple and has less dependency on parameters to adjust. The solar and wind units are committed to ascertaining their share for uninterrupted supply. The price penalty method is applied to unify the emission of gaseous pollutants due to thermal generation with operating costs. To reduce the use of coal, renewable energy sources have been considered in this problem which results in reducing the pollutants' emissions and saving in fuel costs. To solve the dynamic multivariable constrained optimization problem, the forward approach has been implemented. The performance of the proposed algorithm is tested on different electric test systems, and a statistical test justifies the results.

关键词： Exploration Coordinated wind-solar-thermal generation Exploitation artificial hummingbird algorithm Simplex search strategy

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Developing an artificial hummingbird algorithm for Probabilistic Energy Management of Microgrids Considering Demand Response

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Alamir, Nehmedo Kamel, Salah Megahed, Tamer F. Hori, Maiya Abdelkader, Sobhy M. Egypt Japan Univ Sci & Technol Elect Power Engn New Borg El Arab City Egypt Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Mansura Univ Fac Engn Dept Elect Engn Mansoura Egypt Kyushu Univ Platform Inter Transdisciplinary Energy Res Fukuoka Japan

This study proposes an artificial hummingbird algorithm (AHA) for energy management (EM) for optimal operation of a microgrid (MG), including conventional sources and renewable energy sources (RES), with an incentive-based demand response (DR). Due to the stochastic nature of solar and wind output power and the uncertainty of prices and load, a probabilistic EM with hybrid AHA and point estimation method (PEM) is proposed to model this uncertainty by utilizing the normal and Weibull distribution functions. The PEM method is considered a good tool for handling stochastic EM problems. It achieves good results using the same procedures used with the deterministic problems while maintaining low computational efforts. The proposed AHA technique is employed to solve a deterministic incentive DR program, with the goal of reducing the overall cost, which includes the cost of conventional generator fuel and the cost of power transaction with the main grid while taking into account the load demand. Two different case studies are tested. The simulation results of the proposed AHA is compared with the results of well-known metaheuristic algorithms to demonstrate its efficacy. According to AHA's results, a total reduction of energy consumption by 104 KWh for the first case study and 2677 MWh for the second case study is achieved while achieving the lowest overall operating cost. The results demonstrate that the AHA is adequate for tackling the EM problem. Then, to examine the effect of uncertainty on the MG state, a probabilistic EM problem is solved using AHA-PEM.

关键词： energy management Microgrid renewable energy sources generation uncertainty optimization artificial hummingbird algorithm

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An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

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PHYSICAL COMMUNICATION 2022年 55卷

作者： Bhat, Soumya J. Santhosh, K. V. Manipal Acad Higher Educ MAHE Manipal Inst Technol Ctr Cyber Phys Syst Dept Instrumentat & Control Engn Manipal 576104 Karnataka India Shri Madhwa Vadiraja Inst Technol & Management Dept Comp Sci & Engn Bantakal 574115 Karnataka India

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using artificial hummingbird algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios. (c) 2022 Elsevier B.V. All rights reserved.

关键词： artificial hummingbird algorithm Hop based localization Optimization algorithm Range free Reference nodes Wireless sensor network

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Parameter Identification of a Governing System in a Pumped Storage Unit Based on an Improved artificial hummingbird algorithm

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ENERGIES 2022年第19期15卷 6966-6966页

作者： Wang, Liying Zhang, Luyao Zhao, Weiguo Liu, Xiyuan Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056038 Peoples R China

Parameter identification is an important method to establish the governing system of a pumped storage unit. Appropriate parameters will make the governing system obtain better control performance. Therefore, in this study, an improved artificial hummingbird algorithm (IAHA) is proposed for the parameter identification of the governing system in a pumped storage unit. The algorithm integrates two key strategies to improve the optimization ability of the algorithm. First, the Chebyshev chaotic map is employed to initialize the artificial hummingbirds, which in turn increases and enhances the global search capability of the initial population. Second, the Levy flight is introduced in the guided foraging phase to expand the search space and avoid premature convergence. The performance of the proposed IAHA algorithm is compared with that of four other algorithms on 23 standard test functions, and the results show that IAHA has higher accuracy and faster convergence than the other four algorithms. Finally, IAHA was applied to the parameter identification of the governing system of a pumped storage unit to verify the effectiveness of the algorithm in tracking real-world problems.

关键词： artificial hummingbird algorithm Chebyshev chaotic map levy flight pumped storage parameter identification governing system

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An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization

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ADVANCED ENGINEERING INFORMATICS 2022年第0期54卷

作者： Wang, Jiao Li, Yan Hu, Gang Yang, MingShun Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol 5 South Jinhua Rd Xian 710048 Peoples R China

In view of the shortcomings such as slow search speed, low optimization precision and premature convergence of artificial hummingbird algorithm, an enhanced artificial hummingbird algorithm based on golden sine factor named DGSAHA is proposed. Firstly, chaos mapping is used to generate the initial candidate solution to increase the diversity of the population, which lays the foundation for the global search. Then, perturb the individuals by means of the differential variation between individuals on the group, thereby enhancing the diversity of the population, preserving the excellent individuals, eliminating the inferior individuals, and guiding the search process to approach the global optimal solution, avoiding the phenomenon of premature convergence. Finally, the golden sine factor were introduced in the guided foraging stage is conducive to the full exploration of the global optimal solution, reducing the search space for individuals to approach the optimal solution. And, it fa-cilitates the balance between "exploration" and "exploitation" of algorithm. Thereby, the accuracy and speed of the DGSAHA can be improved to a certain extent. 25 classic functions, the CEC2014 and CEC2019 benchmark functions were tested, and several representative meta-heuristic algorithms and its improved algorithm are compared for evaluate the validity of DGSAHA. Meanwhile, the dimensional scalability of the variable -dimensional test function is discussed. The results of non-parametric statistical analysis and performance index show that DGSAHA in this paper has better comprehensive optimization performance, significantly im-proves the search speed and convergence precision, and has strong ability to get rid of the local optimal solution. Finally, the performance of DGSAHA and the practicability of truss structure are answered by three engineering examples of plane and space truss topology optimization problem. This optimization problem considers not only the static constraints such a

关键词： artificial hummingbird algorithm Differential mutation operation Chaotic map Golden sine factor Truss topological optimization

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An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 398卷

作者： Zhao, Weiguo Zhang, Zhenxing Mirjalili, Seyedali Wang, Liying Khodadadi, Nima Mirjalili, Seyed Mohammad Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056038 Hebei Peoples R China Univ Illinois Prairie Res Inst Champaign IL 61820 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea Iran Univ Sci & Technol Dept Civil Engn Tehran Iran Concordia Univ Montreal PQ Canada

artificial hummingbird algorithm (AHA) is a recently developed bio-based metaheuristic and it shows superior performance in handling single-objective optimization problems. Despite the merit, this algorithm can only solve problems with one objective. To solve complex multi-objective optimization problems, including engineering design problems, a multi-objective AHA (MOAHA) is developed in this study. In MOAHA, an external archive is employed to save Pareto optimal solutions, and a dynamic elimination-based crowding distance (DECD) method is developed to maintain this archive to effectively preserve the population diversity. In addition, a non-dominated sorting strategy is merged with MOAHA to construct a solution update mechanism, which effectively refines Pareto optimal solutions for improving the convergence of the algorithm. The superior results over 7 competitors on 28 benchmark functions in terms of convergence, diversity and solution distribution are demonstrated with a suite of comprehensive tests. The MOAHA algorithm is also applied to 5 real-world engineering design problems with multiple objectives, demonstrating its superiority in handling challenging real-world multi-objective problems with unknown true Pareto optimal solutions and fronts. The source code of MOAHA is publicly available at https://*** ***/matlabcentral/fileexchange/113535-moaha-multi-objective-artificial-hummingbird-algorithm and https://seyedalimirja ***/aha.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization artificial hummingbird algorithm Engineering design problems Convergence and diversity Dynamic elimination-based crowding distance Non-dominated sorting

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A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems

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APPLIED ENERGY 2022年 323卷

作者： Fathy, Ahmed Jouf Univ Dept Elect Engn Fac Engn Sakaka 42421 Saudi Arabia Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig 44519 Egypt

Improving the performance of the electric distribution network is essential to meet the needs of the customer and guarantee the service continuity. Installing generators with small sizes known as distributed generators (DGs) can contribute to enhance the network operation by mitigating the network loss and improving the voltage profile. Integrating these generators in inappropriate places can cause serious consequences to the network operation. Therefore, this paper proposes a novel metaheuristic approach of artificial hummingbird algorithm (AHA) to identify the best locations and sizes of biomass-based DGs in radial distribution network. The proposed approach has enriched exploration and exploitation phases that enhancing its search capability and avoiding stuck in local optima. The network active power loss and the voltage deviation are selected as the targets to be minimized. Moreover, a new version of AHA is programmed to solve multi-objective problem with the purpose of mitigating both targets. The analysis is conducted on three radial distribution networks of IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. Three scenarios are implemented in each network, the first one is minimizing the active power loss, the second one is mitigating the voltage deviation, and the last one is multi-objective problem. Also, biomass-based DGs with unity, fixed, and optimal power factors are analyzed. Excessive comparison to fractal search algorithm, particle swarm optimizer, genetic algorithm, the whale optimization algorithm, sperm swarm optimization, tunicate swarm algorithm, pathfinder algorithm, seagull optimization algorithm, and sine cosine algorithm, multi-objective water cycle algorithm, multi-objective grey wolf optimizer, and multi-objective sparrow search algorithm is conducted. Moreover, statistical tests of Wilcoxon, Friedman, ANOVA, and Krus-kal Wallis are performed to assess the performance of the proposed approach. The gotten results confirmed the preference and compe

关键词： Radial distribution network artificial hummingbird algorithm Biomass -based DGs

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A multi-objective artificial hummingbird algorithm for dynamic optimal volt-var controls for high electric vehicle load penetration in a photovoltaic distribution network

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： Kondisetti, Lalitha Katragadda, Swarnasri Dr. YSR. ANU College of Engineering and Technology Acharya Nagarjuna University Andhra Pradesh Guntur 522510 India Department Electrical & Electronics Engineering R.V.R. & J.C. College of Engineering Andhra Pradesh Guntur 522 019 India

Renewable energy systems (RESs) and electric vehicles (EVs) have become global necessities to meet the sustainable development goals (SDGs). Because they are intermittent and stochastic, these two technologies have caused power grid operation and regulation challenges despite their environmental benefits. A new combined dynamic optimal network reconfiguration (DONR) and capacitor bank switching (CBS) optimization technique reduces solar and multi-type load effects on the distribution network performance. Considering substantial EV load penetrations, ONR and CBS should coordinate to reduce energy loss, improve the voltage profile, and save money during 24 h operation. The artificial hummingbird algorithm (AHA) solves this difficult optimization problem for the first time. An enhanced IEEE 33-bus benchmark test system was used to simulate various instances to evaluate the computational characteristics of the AHA and compare it with other similar algorithms. The obtained results demonstrate the superiority of the combined DONR and CBS for real-time scenarios over any individual approach. © 2024 The Author(s)

关键词： Active distribution network artificial hummingbird algorithm Capacitor bank switching Dynamic optimal network reconfiguration Electric vehicles Multi-objective optimization Photovoltaic systems

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