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Application of artificial hummingbird algorithm in a renewable energy source integrated multi-area power system considering Fuzzy based tilt integral derivative controller

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 4卷

作者： Bhagat, Sanjeev Kumar Saikia, Lalit Chandra Babu, Naladi Ram Electrical Engineering Department NIT Silchar 788010 India Department of Electrical and Electronics Engineering Aditya Engineering College East-Godavari Andhra Pradesh Surampalem 533437 India

This paper demonstrates the application of a novel artificial hummingbirds’ algorithm (AHA) and fuzzy (F) based tilt-integral-derivative (F-TID) controller in a renewable energy source integrated multi-area power system for the first time. Also, the chest time constant of steam turbine are calculated based sliding pressure mode operation of the superheater and it has been found that the chest time constant of steam turbine depends on plant loading. The system dynamics with the proposed controller are compared to those of existing controllers such as proportional-integral-derivative (PID) and TID, and it is revealed that the system with proposed F-TID controller has better system dynamics. The comparison of system dynamic with F-TID considering grasshopper algorithm (GHA), bird swarm algorithm (BSA) and AHA yield that the system dynamic with AHA outperformed. Further, a Friedman test has been also performed to validate the proposed algorithm. The study on various loading of thermal unit considering sliding pressure mode of operation revealed that, when loading increases toward or above its rated value the system dynamic improves. The effects of dish-stirling-thermal-system (DSTS) and wind-turbine-system (WTS) integration on system performance are also analyzed, and it has been found that the integration of renewable energy sources supports in a generation and it shows approximately 6% improvement in frequency profile along with 8% improvement in tie-power responses. The comparative analysis of system dynamics with AC tie-line, conventional high voltage direct current (HVDC), and accurate model of HVDC link reveals the better dynamics performance of the latter. Furthermore, the robustness of proposed F-TID controller is evaluated by sensitivity analysis and it is inferred that the proposed controller provides stables responses under varied loading condition. © 2023 The Author(s)

关键词： artificial hummingbird algorithm Automatic generation control Dish-stirling thermal system Fuzzy control High voltage direct current Tilt controller Wind turbine system

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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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A Multiobjective artificial-hummingbird-algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources

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ENERGIES 2022年第23期15卷 9250页

作者： Waleed, Umar Haseeb, Abdul Ashraf, Muhammad Mansoor Siddiq, Faisal Rafiq, Muhammad Shafique, Muhammad Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Elect Engn Topi 23460 Pakistan Univ Engn & Technol Dept Elect Engn Taxila 47050 Pakistan Brunel Univ London Dept Civil & Environm Engn Uxbridge UB8 3PH England

This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework.

关键词： artificial hummingbird algorithm artificial intelligence optimal reactive power dispatch optimal power flow on-load tap-changing transformer

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Binary Hybrid artificial hummingbird with Flower Pollination algorithm for Feature Selection in Parkinson’s Disease Diagnosis

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Journal of Bionic Engineering 2024年第2期21卷 1003-1021页

作者： Liuyan Feng Yongquan Zhou Qifang Luo College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Xiangsihu College of Guangxi University for Nationalities Nanning532100GuangxiChina Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on *** experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease ***,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer *** this paper,a Binary Hybrid artificial hummingbird and Flower Pollination algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech ***,combining FPA with artificial hummingbird algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local ***,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal ***,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)*** this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease *** with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

关键词： artificial hummingbird algorithm Flower pollination algorithm Feature selection Parkinson’s disease Meta-heuristic

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A new hybrid artificial hummingbird-simulated annealing algorithm to solve constrained mechanical engineering problems

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MATERIALS TESTING 2022年第7期64卷 1043-1050页

作者： Yildiz, Betul Sultan Mehta, Pranav Sait, Sadiq M. Panagant, Natee Kumar, Sumit Yildiz, Ali Riza Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 Gujarat India King Fahd Univ Petr & Minerals Dhahran Saudi Arabia Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand Univ Tasmania Launceston Tas 7250 Australia Uludag Univ Bursa Uludag Univ Dept Mech Engn TR-16059 Bursa Turkey

Nature-inspired algorithms known as metaheuristics have been significantly adopted by large-scale organizations and the engineering research domain due their several advantages over the classical optimization techniques. In the present article, a novel hybrid metaheuristic algorithm (HAHA-SA) based on the artificial hummingbird algorithm (AHA) and simulated annealing problem is proposed to improve the performance of the AHA. To check the performance of the HAHA-SA, it was applied to solve three constrained engineering design problems. For comparative analysis, the results of all considered cases are compared to the well-known optimizers. The statistical results demonstrate the dominance of the HAHA-SA in solving complex multi-constrained design optimization problems efficiently. Overall study shows the robustness of the adopted algorithm and develops future opportunities to optimize critical engineering problems using the HAHA-SA.

关键词： artificial hummingbird algorithm planetary gear train simulated annealing ten bar truss problem vehicle crash problem

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artificial hummingbird optimizer as a novel adaptive algorithm for identifying optimal coefficients of digital IIR filtering systems

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2023年

作者： Ekinci, Serdar Izci, Davut Kayri, Murat Batman Univ Dept Comp Engn Batman Turkiye Middle East Univ MEU Res Unit Amman Jordan Van Yuzuncu Yil Univ Dept Comp & Instruct Technol Educ Van Turkiye Batman Univ Dept Comp Engn TR-72100 Batman Turkiye

This paper introduces the artificial hummingbird algorithm (AHA) as a novel and efficient algorithm for designing digital infinite impulse response (IIR) filtering systems. IIR filters are commonly used in various applications, and their design often involves complex error surfaces and coefficients. The AHA aims to address these challenges by optimizing the IIR model using mean squared error (MSE) cost function. Four benchmark examples are considered for evaluation by comparing both the same and reduced order cases of the IIR model. The results are compared against recent and efficient algorithms named prairie dog optimization, whale optimization algorithm, and artificial bee colony algorithm. The evaluation includes an analysis of the obtained coefficients, statistical measures, and convergence profiles. The findings show that the AHA outperforms the competing algorithms, achieving more accurate models and providing better statistical performance. Additionally, the AHA consistently converges to the lowest MSE values for each case. Further assessments are conducted using various examples from the literature, which validate the AHA's superior ability in digital IIR filtering design. The algorithm demonstrates more accurate identification and achieves lower MSE values compared to alternative methods which highlight the promising potential of the AHA for efficient and precise digital IIR filtering system design.

关键词： Digital IIR filters system identification artificial hummingbird algorithm swarm-based optimization

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artificial hummingbird-Based Optimisation with Advanced Crowding Distance of Energy Reduction in the Polyethylene Reactors

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2024年第1期8卷 271-284页

作者： Rohman, Fakhrony Sholahudin Alwi, Sharifah Rafidah Wan Muhammad, Dinie Idris, Iylia Zahan, Khairul Azly Murat, Muhamad Nazri Azmi, Ashraf Univ Teknol Malaysia Res Inst Sustainable Environm RISE Proc Syst Engn Ctr UTM PROSPECT Johor Baharu 81310 Malaysia Univ Teknol Malaysia UTM Fac Engn Sch Chem & Energy Engn Johor Baharu 81310 Johor Malaysia Univ Teknol MARA Coll Engn Sch Chem Engn Shah Alam 40450 Selangor Malaysia Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan Univ Tun Hussein Onn Malaysia Fac Engn Technol Batu Pahat 86400 Johor Malaysia Univ Sains Malaysia Sch Chem Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia

Ethylene is polymerised by free radicals under extreme conditions of high pressure and temperature to produce low-density polyethylene LDPE. Considering the requirement for high compression power and heating-cooling elements, combined with depleting fossil fuel and climate change issues, an approach is needed to trade-off these issues. As such, an effective approach of multi-objective optimisation study to obtain the optimum production of the LDPE with minimum energy consumption is proposed in this work. The multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance (MOAHA-DECD) executes within ASPEN Plus-MATLAB environment for energy saving of low-density polyethylene (LDPE) production. Three problems are addressed: minimise energy cost and maximise productivity for problem 1 (P1);minimising energy cost and maximising conversion for problem 2 (P2);and minimising energy cost, maximising productivity, and maximising conversion for problem 3 (P3). The inlet pressure, the mass flow rate of Initiator 1 (tert-butyl peroxypivalate, TBPPI), and the mass flow rate of Initiator 2 (tert-butyl 3,5,5trimethyl-peroxyhexaonate (TBPIN)) of the reacting zones (zone 3 and zone 5) are considered as decision variables. Pareto solutions obtained are arrayed across the entire Pareto front (PF) with an even sweep and diverse points. Based on the results, the highest productivity, lowest energy cost, and highest conversion are 554.958 Mil. RM/year, 61.388 Mil. RM/year, and 0.320. The decision variable plots show that the mass flow rate of the initiator at the end zone of the reactor highly impacts the optimal option. For the next study, the generated Pareto allows decision-makers to select the most acceptable solution based on their preferences to trade-off economic, energy, and environmental issues.

关键词： Low-density polyethylene Tubular reactor Multi-objective optimisation artificial hummingbird algorithm Pareto front

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Simulated Annealing Aided artificial hummingbird Optimizer for Infinite Impulse Response System Identification

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IEEE ACCESS 2023年 11卷 88627-88636页

作者： Ekinci, Serdar Izci, Davut Yilmaz, Musa Batman Univ Dept Comp Engn TR-72100 Batman Turkiye Middle East Univ MEU Res Unit Amman 11831 Jordan Batman Univ Dept Elect & Elect Engn TR-72100 Batman Turkiye Univ Calif Riverside Bourns Coll Engn Ctr Environm Res & Technol Riverside CA 92521 USA

Infinite impulse response (IIR) systems, with their ability to model intricate system behaviors, have proven to be a powerful class of digital filters. However, accurately identifying the optimal filter parameters for system emulation remains a challenge. Metaheuristic algorithms have emerged as effective tools for parameter optimization in IIR filter design, allowing for the exploration of parameter spaces and the discovery of suitable filter sets. This paper introduces a novel adaptive algorithm, named simulated annealing aided artificial hummingbird optimizer (AHA-SA), which combines the strengths of the artificial hummingbird algorithm (AHA) and simulated annealing (SA). The synergistic integration of AHA and SA in the AHA-SA optimizer enables efficient search space exploration, rapid convergence, and the attainment of precise solutions. Extensive experiments demonstrate the superiority of the AHA-SA optimizer over competitive algorithms, both in terms of solution quality and convergence speed. The proposed optimizer presents a promising solution for optimization problems in various domains, with its simplicity, intuitive workflow, and potential for widespread adoption.

关键词： artificial hummingbird algorithm simulated annealing algorithm digital filter design metaheuristics

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Hybrid algorithm for directly detecting and classification of multiple power quality disturbances

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 242卷

作者： Altintasi, Cagri Erzincan Binali Yildirim Univ Dept Elect & Elect Engn TR-24002 Erzincan Turkiye

Detecting potential power quality (PQ) events that may occur in the network is an important issue to provide safe electricity to customers. In this study, anew hybrid algorithm named adaptive fitness distance balance hybrid artificial hummingbird algorithm-artificial rabbit optimization (AFDB-AHAARO) algorithm is introduced for the detection and classification of PQ events. A hybrid algorithm is developed to improve the early convergence of the AHA algorithm by expanding the search to avoid local minima. The proposed method, unlike the optimization algorithms used in the literature, is directly applied to the detection and classification of PQ events. The performance of the proposed algorithm is investigated by 6000 different single and multiple PQ events in the Matlab in noisy and noiseless environments, considering the IEEE-1159 standards. The obtained results are compared with the AHA algorithm and the methods proposed in the literature, and it is shown that the proposed method outperforms in noisy environments. It also provides information about the time interval of PQ events. The validity of the proposed algorithm in real systems is demonstrated by testing it on the voltage signal with flicker disturbance obtained from the electrical network and on experimentally generated sag and swell events.

关键词： Power quality disturbance artificial hummingbird algorithm artificial rabbit optimization Hybrid artificial optimization algorithm Adaptive fitness distance balance

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Dynamic Resource Provisioning in Cloud Computing Using Optimized Wasserstein Deep Convolutional Generative Adversarial Networks

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第4期36卷

作者： Santhiya, C. Padmavathi, S. Thiagarajar Coll Engn Dept Comp Sci & Engn Thiruparankundram Tamil Nadu India Thiagarajar Coll Engn Dept Informat Technol Thiruparankundram Tamil Nadu India

Cloud computing (CC) has revolutionized the way resources are managed and delivered by providing scalable, on-demand services. However, dynamic resource provisioning remains a complex challenge due to unpredictable workloads, varying user demands, and the need to maintain cost efficiency. Traditional resource allocation techniques lack the adaptability required to optimize resource usage under dynamic conditions. This manuscript presents a novel approach for dynamic resource provisioning using an Optimized Wasserstein Deep Convolutional Generative Adversarial Network (DRP-WDCGAN-AHBA). Initially, the input data are collected from the Grid Workloads Dataset, which provides a comprehensive representation of workload patterns in cloud environments. The input data undergo rigorous preprocessing using Adaptive Self-Guided Filtering (ASGF) to ensure data quality. Then, Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN) is used to forecast CPU utilization over specified time intervals of 5, 15, 30, and 60 min. The Adaptive Hybrid Bat algorithm (AHBA) is employed to optimize resource allocation dynamically and ensure efficient utilization. The proposed DRP-WDCGAN-AHBA model attains 20.36%, 18.63%, and 21.24% lower energy consumption and 16.78%, 23.64%, and 26.32% lower response time when compared with existing models, such as Multi-agent QoS-aware autonomic resource provisioning method BPM in containerized multi-cloud environs for elastic (DRP-QoS-EDSAE), Multi-objective dependent Scheduling Method for Effective Resource Utilization in Cloud Computing (DRP-LS-CSO-ARNN), and Energy-aware fully adaptive resource provisioning in collaborative CPU-FPGA cloud environs: Journal of Parallel and Distributed Computing (EFARP-CPU-FPGA).

关键词： Adaptive Self-Guided Filtering artificial hummingbird algorithm cloud computing dynamic resource provisioning software as a service virtual machines Wasserstein Deep Convolutional Generative Adversarial Network

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