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Parameter estimation study of polymer electrolyte membrane fuel cell using artificial hummingbird algorithm

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第8期237卷 1956-1967页

作者： Celik, Muhammet Soylu, Selim Aksaray Univ Dept Mech Engn Aksaray Turkey Aksaray Univ Dept Elect & Elect Engn Aksaray Turkey

This study represents a comprehensive investigation of the performance of artificial hummingbird algorithm for parameter estimation for Polymer Electrolyte Membrane Fuel Cell. With this purpose, four commercial fuel cell systems which were widely preferred in the literature such as NedStack PS6 (Case-I), 250 W fuel cell stack (Case-II), Horizon 500 W (Case-III), and BCS 500 W (Case-IV) were chosen. In order to compare the performance of this algorithm, seven well-known optimization techniques including artificial Bee Colony, Salp Swarm Optimization, Particle Swarm Optimization, Gray Wolf Optimization, Genetic algorithm, Harris Hawks Optimization, and Whale Optimization algorithm were used. The sum of the squared errors, computational speed, and statistical measurements were calculated for the performance comparison. In this context, the best SSE values were found as 2.06556, 5.25017, 0.02477, 0.01170 for Case-I, Case-II, Case-III, and Case-IV, respectively. The best standard deviation value was found as 1e(-6) for the Case-III. Based on the obtained results, the artificial hummingbird algorithm established itself as a competitive optimization technique for parameter estimation study of PEMFC in terms of computational speed and robustness.

关键词： Polymer electrolyte membrane fuel cells artificial hummingbird algorithm parameter estimation fuel cell renewable energy

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Optimal scheduling of short-term hydrothermal with integration of renewable energy resources using Lévy spiral flight artificial hummingbird algorithm

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ENERGY REPORTS 2023年 10卷 2756-2777页

作者： Jamal, Raheela Zhang, Junzhe Men, Baohui Khan, Noor Habib Youssef, Abdel-Raheem 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 South Valley Univ Fac Engn Dept Elect Engn Qena 83523 Egypt Sohag Univ Fac Engn Dept Elect Engn Sohag 82524 Egypt Univ Jaen Dept Elect Engn Eps Linares 23700 Jaen Spain Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt

Short-term hydrothermal scheduling (STHS) is taking an important consideration in the power system for economics operation. The solution to the STHS problem gives time-varying scheduling of power generation with available hydro and thermal units aims to attain the cost saving of thermal units for a given time period. It is a non-convex optimization problem with a set of inequality and equality constrains such as load balancing, transmission losses as well as valve-point effect to the thermal power units. The permeation of renewable energy is frequently used into the power networks aims to reduce the cost of generation and emissions. The two foremost resources such as wind and photovoltaic are frequently used in the power networks. In the research, the use of both powers integrated into the STHS problem to reduce the fuel cost ($) by using the modified artificial hummingbird algorithm (MAHA). To modelled the uncertainty in solar irradiance and windspeed, the lognormal and Weibull distribution are used. The novel MAHA is based on two enhancing methods;(1). updating the positions of the hummingbirds in stochastic motion via Levy flight walk and (2). updating the location of the hummingbirds via pitch adjustment motion. The MAHA is further tested on the four STHS cases such as;four hydro one thermal (non-vple and vple), four hydro three thermal (vple without and with losses), four hydro three thermal with integration of RERs including direct cost only (VPLE without and with losses) and four hydro three thermal with uncertainty of RERs including direct, reverse and penalty costs (vple without losses), respectively. Moreover, for validation the performance of MAHA is further compared to the outcomes of AHA and other state-of-the-arts techniques. (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： artificial hummingbird algorithm Renewable Energy Resources Wind power Photo-voltaic Short-term hydrothermal scheduling

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Quantum artificial hummingbird algorithm for Feature Selection of Social IoT

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IEEE ACCESS 2023年 11卷 66257-66278页

作者： Abd Elaziz, Mohamed Dahou, Abdelghani Al-Betar, Mohammed Azmi El-Sappagh, Shaker Oliva, Diego Aseeri, Ahmad O. Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Galala Univ Fac Comp Sci & Engn Suez Egypt Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman 11831 Jordan Univ Ahmed Draia Math & Comp Sci Dept Adrar 01000 Algeria Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid 19117 Jordan Benha Univ Fac Comp & Artificial Intelligence Informat Syst Dept Banha 13518 Egypt Univ Guadalajara Dept Innovac Basada Informac & Conocimiento CUCEI Guadalajara 44430 Mexico Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 11942 Saudi Arabia

The Internet of Things (IoT) benefits from social networking platforms in establishing and enhancing social-oriented services, information, and autonomous social relationships. Social IoT (SIoT) systems can boost the user experience in the real world in several applications, including healthcare, transportation, and entertainment. However, the collected data from various interconnected SIoT systems is massive, demanding robust and efficient processing algorithms, feature extraction, selection, and inference. This work presents an enhanced artificial hummingbird algorithm (AHA) for feature selection (FS). The enhanced version of AHA is performed using the advantages of Quantum-based optimization. The main aim of using Quantum is to improve the population's exploration ability while discovering feasible regions. Extensive experiments utilizing eighteen UCI datasets were conducted to validate the developed FS method, QAHA. The QAHA is compared with other FS methods, and the experimental established its efficiency. Moreover, a set of four datasets from SIoT are used to evaluate the applicability of QAHA to the real-world setting. The results using these datasets indicate the high performance of QAHA to increase the accuracy by decreasing the number of features. In the case of UCI datasets, the average accuracy of the developed QAHA is 93% among the eighteen datasets. Whereas, In the case of the SIoT datasets, the developed QAHA has an accuracy of nearly 90.7%, 98.7%, 92.2%, and 84.6% for the Trajectory, GAS sensors, Hepatitis, and MovementAAL datasets, respectively.

关键词： artificial hummingbird algorithm feature selection swarm optimization quantum-based optimization social Internet of Things

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Improvisation of artificial hummingbird algorithm through incorporation of chaos theory in intelligent optimization of fractional order PID controller tuning

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International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Sarma, Hrishikesh Bardalai, Aroop Department of Electrical Engineering Assam Engineering College Assam Guwahati India

artificial hummingbird algorithm (AHA) is one of the recent bio-inspired meta-heuristic algorithms which is based on hummingbirds’ intelligent behaviours. Just like many meta-heuristic algorithms, it also suffers from freezing in local optima and slow convergence speed. In this paper, the authors have proposed a novel chaotic artificial hummingbird algorithm (ChAHA) obtained by incorporating chaos theory in the original AHA with the aim of escaping it from local minima stagnation along with high convergence rate and more precise results. Firstly, detailed studies have been performed on six different unimodal and multimodal constrained benchmark functions by employing ten different chaotic test mappings in order to determine the most enhanced and efficient one. Later, statistical testing and graphical analysis prove that incorporation of chaotic maps (especially tent map) in AHA improves the original AHA by showing promising performance. Finally, the performance of the ChAHA (with tent map) is also validated by finding the optimum gain values of a fractional order proportional-integral-derivative (FOPID) controller, meticulously tailored to meet the specific requirements of DC motor speed control in MATLAB/Simulink. It has been unambiguously affirmed that the closed loop system with the proposed ChAHA-FOPID controller has better performance than certain pre-existing controllers such as grey wolf optimization based FOPID (GWO-FOPID), atom search optimization based FOPID (ASO-FOPID) and manta ray foraging optimization based FOPID (MRFO-FOPID) controllers. Finally, robustness analysis is also carried out with parameter variations of DC motor and the final simulation results validate the superiority of the proposed approach. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： artificial hummingbird algorithm Chaotic map DC motor Fractional order PID controller ITAE objective function Robustness analysis

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Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees

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Journal of Road Engineering 2024年第2期4卷 224-233页

作者： Ikenna D.Uwanuakwa Ilham Yahya Amir Lyce Ndolo Umba Department of Civil Engineering Near East UniversityMersin 10Turkey Faculty of Sciences University of LubumbashiLubumbashiThe Democratic Republic of Congo

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt *** a substantial dataset from NCHRP Report-547,the model was trained and rigorously *** metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and *** benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced *** achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model *** features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's *** study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key *** the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further ***,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering.

关键词： Asphalt Dynamic modulus Prediction artificial hummingbird algorithm Boosted tree

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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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Single-Objective Optimal Power Flow Using artificial hummingbird algorithm 3

Single-Objective Optimal Power Flow Using Artificial Humming...

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3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Avvari, Ravi Kumar Vnr Vignana Jyothi Institute of Engineering and Technology Department of Electrical & Electronics Engineering Hyderabad500090 India

ISBN: (纸本)9798350354379

This study introduces a novel artificial hummingbird algorithm (AHA) for addressing the optimal power flow (OPF) problem. The objectives of this OPF challenge are to minimize total generation costs, emissions, and active power losses. A penalty-based constraint handling method is used to address various restrictions of the OPF problem. The suggested algorithm has undergone testing on the standard IEEE 30-bus test system. The results obtained with the artificial hummingbird algorithm (AHA) are contrasted with those from the grey wolf optimisation (GWO) method. The comparison demonstrates the superiority of the artificial hummingbird algorithm (AHA) relative to the GWO algorithm. © 2024 IEEE.

关键词： Active Power Loss artificial hummingbird algorithm Emission Generation Cost OPF

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artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 388卷 114194-114194页

作者： Zhao, Weiguo Wang, Liying Mirjalili, Seyedali Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056038 Hebei Peoples R China Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

A new bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is proposed in this work to solve optimization problems. The AHA algorithm simulates the special flight skills and intelligent foraging strategies of hummingbirds in nature. Three kinds of flight skills utilized in foraging strategies, including axial, diagonal, and omnidirectional flights, are modeled. In addition, guided foraging, territorial foraging, and migrating foraging are implemented, and a visit table is constructed to model the memory function of hummingbirds for food sources. AHA is validated using two sets of numerical test functions, and the results are compared with those obtained from various algorithms. The comparisons demonstrate that AHA is more competitive than other meta-heuristic algorithms and determine high-quality solutions with fewer control parameters. Additionally, the performance of AHA is validated on ten challenging engineering design cases studies. The results show the superior effectiveness of AHA in terms of computational burden and solution precision compared with the existing optimization techniques in literature. The study also explores the application of AHA in hydropower operation design to further demonstrate its potential in practice. The source code of AHA is publicly available at https://***/aha and https://*** ***/matlabcentral/fileexchange/101133-artificial-hummingbird-algorithm?s_tid=srchtitle. (c) 2021 Elsevier B.V. All rights reserved.

关键词： artificial hummingbird algorithm Engineering optimization Swarm intelligence Meta-heuristics Bio-inspired computing algorithm Benchmark Genetic algorithm

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Parameter Estimation of Solar Modules Operating Under Outdoor Operational Conditions Using artificial hummingbird algorithm

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IEEE ACCESS 2022年 10卷 51299-51314页

作者： Haddad, Sofiane Lekouaghet, Badis Benghanem, Mohamed Soukkou, Ammar Rabhi, Abdelhamid MSB Jijel Univ Elect Dept RE Lab Jijel 18000 Algeria Islamic Univ Madinah Fac Sci Phys Dept Medina 41477 Saudi Arabia Univ Picardie Jules Verne MIS Lab F-80000 Amiens France

In this article, an artificial algorithm called hummingbirds optimization method, named AHA algorithm, is settled to extract accurately the parameters of PV modules under outdoor operational conditions. AHA is the main contribution in this work, regarding its efficiency and good performance in terms of standard deviation (StD), root mean square error (RMSE), sum of squared error (SSE), maximum number of iterations (MaxIt), particularly, for extracting parameter from modules operating in real conditions, and under different temperature and irradiance levels. The AHA is applied on a Polycrystalline-solar panel module type 320W-72P at real operating conditions and on a PV array of three polycrystalline PV modules connected in series. PV cells of the same modules are assumed operating under the same conditions where they share the same electric current and voltage values. In this last pattern, eight different scenarios are chosen. In the first five scenarios, the temperatures are (46.97, 44.23, 42.87, 40.59 and 30.60 degrees C), and the irradiance varies, such as (910, 800.57, 614.13, 415.1 and 200.87W/m2), respectively. In the other three testing scenarios, the solar irradiance is equal (803 W/m2), and the temperature differs, such as T=47.93 degrees C, 53.38 degrees C and 36.62 degrees C. Lower values of root mean square errors (RMSE) are achieved (9.8602 x 10(-4) and 2.572533 x 10(-2) for RTC France PV cell and the 320W-72P module respectively) with 6000 iterations. Moreover, all the eight scenarios are lower than 7.245916 x 10(-2) for the last case study. Moreover, results show that we could recommend the AHA algorithm as an advanced and efficient method for dealing with real time parameter optimization of photovoltaic modules. In fact, a high closeness between the simulated and the experimental curves is achieved, which indicates the perfectness of this optimization method. Finally, the proposed AHA algorithm can be engaged as tools for the best designing of PV sys

关键词： artificial hummingbird algorithm photovoltaic cells physical parameters PV extraction

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SPOAHA: Spark Program Optimizer Based on artificial hummingbird algorithm 16th

SPOAHA: Spark Program Optimizer Based on Artificial Hummingb...

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Wang, Miao Zhen, Jiteng Ma, Yupeng Huang, Xu Zhang, Hong Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Peoples R China

ISBN: (纸本)9783031402883;9783031402890

In this era of the Internet of Things (IoT), a large number of sensor devices collect and generate various sensing data over time. It is very essential to mine fresh information by analyzing large amounts of data, predict the future, and make correct decisions. Therefore, a growing number of data-intensive computing frameworks have been proposed, such as Hadoop, Spark, Flink, etc. Rather than reading and writing files to disks, Spark processes data with a memory-based computing framework to improve the performance, which has attracted more attention from researchers. However, due to a wealth of operators provided by Spark, a certain application can be implemented in various ways, which also show big differences in performance. Therefore, tuning a Spark application is a very error-prone and time-consuming process, and requires developers to have a deep understanding of Spark's operating principles and characteristics. In this paper, we summarize a series of rules such as operator reordering and operator replacement to design and implement a Spark program optimizer, called SPOAHA, based on the artificial hummingbird algorithm. Experimental results show that without changing the semantics of the original program, the optimized program dramatically reduces the amount of data involved in the shuffling period, and speeds up the execution time by up to 2.7x.

关键词： Spark Big Data Program Optimization artificial hummingbird algorithm

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