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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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Fabric Wrinkle Objective Evaluation Model with Random Vector Function Link Based on Optimized artificial hummingbird algorithm

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JOURNAL OF NATURAL FIBERS 2023年第1期20卷 Article: 2163026页

作者： Zhou, Zhiyu Hu, Yanjun Zhu, Zefei Wang, Yaming Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China Lishui Univ Zhejiang Key Lab DDIMCCP Lishui Peoples R China Zhejiang Sci tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China

With the more and more amelioration of our quality of life, our needs for clothing have altered from having clothes to wearing good-looking, among which the wrinkle resistance of clothing fabric owns a giant effect on the beauty of clothing. Nowadays, artificial subjective evaluation is mainly used to evaluate the wrinkle grade of garment fabrics in the textile industry. This evaluation method owns the shortcoming of poor accuracy, being time-consuming and poor objectivity. For solving this problem, it is very important to put forward an objective evaluation model of fabric wrinkle grade. In this paper, we proposed a fabric wrinkle objective evaluation model with the optimized random vector functional link. The model applies DarkNet19 deep neural network to abstract the high-order visual features of the wrinkled surface image of the fabric, uses the improved artificial hummingbird optimization algorithm to ameliorate the import bias and weight of the random vector function link's hidden layer, and uses L (2,1) norm regularization computes output weights for random vector function links. The relative tests reveal that the objective evaluation model of fabric wrinkles put forward in this paper has excellent performance.

关键词： Fabric wrinkle objective evaluation DarkNet19 artificial hummingbird algorithm random vector functional link parameter optimization Gaussian mutation quasi-reflected based learning

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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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Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions

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AIN SHAMS ENGINEERING JOURNAL 2023年第2期14卷

作者： Ramadan, Ashraf Ebeed, Mohamed Kamel, Salah Ahmed, Emad M. Tostado-Veliz, Marcos Aswan Univ Dept Elect Engn Aswan 81542 Egypt Sohag Univ Fac Engn Sohag 82524 Egypt Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Saudi Arabia Univ Jaen Dept Elect Engn Jaen Spain

Renewable distributed generators (RDGs) have been widely used in distribution networks for technolog-ical, economic, and environmental reasons. The main concern with renewable-based distributed genera-tors, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distri-bution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo sim-ulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with con-sidering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems.

关键词： Uncertainties artificial hummingbird algorithm Backward reduction algorithm Monte -Carlo simulation Renewable Energy Wind Solar

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

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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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Complex-valued artificial hummingbird algorithm for global optimization and short-term wind speed prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Feng, Liuyan Zhou, Yongquan Luo, Qifang Wei, Yuanfei Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Univ Nationalities Xiangsihu Coll Nanning 530225 Peoples R China Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Environmental pollution and energy depletion have spurred the exploration of renewable energy sources. Wind energy, with its sustainability and eco-friendliness, stands out as a competitive option. However, its effectiveness relies on accurately predicting the fluctuating wind speeds, necessitating ongoing research in this area. In order to improve the accuracy of wind speed prediction, this article presents the first Complex-valued version of the artificial hummingbird algorithm (CAHA), which utilizes the idea of doubles to increase the initial population diversity and enhance the algorithm's performance. Additionally, the article employs the CAHA to optimize the parameters of artificial Neural Networks (ANNs). This marks the first application of the hybrid model to address the short-term wind speed prediction problem. The experiments begin with a performance evaluation of CAHA using the CEC2022 test set. Then, the CAHA is employed to optimize the parameters of six distinct ANNs to select the ANN model that can produce the best prediction result when synergized with CAHA. The results determine that the hybrid model based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) exhibits the best prediction performance. Furthermore, the performance of CAHA is compared with other classical optimization algorithms in short-term wind speed prediction problems based on ANFIS. Statistical results show that the ANFIS-CAHA model significantly improves the accuracy of short-term wind speed prediction, making it a potent tool for the integration of wind energy into smart grid engineering.

关键词： artificial hummingbird algorithm Complex -valued encoding artificial neural network Short-term wind speed prediction Metaheuristic

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Enhanced artificial hummingbird algorithm with chaotic traversal flight

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SCIENTIFIC REPORTS 2024年第1期14卷 1-37页

作者： Du, Juan Zhang, Jilong Li, Shouliang Yang, Zhen Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent in traditional meta-heuristic algorithms, this study presents the enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs chaotic ergodicity within the foundational framework of the conventional artificial hummingbird algorithm. This approach implements chaotic motion within local regions of the solution space, ensuring a thorough exploration of potential optima and preventing algorithmic stagnation at local maxima by guaranteeing a non-repetitive traversal of all search states. This study also analyzes the intrinsic mechanisms by which eight different chaotic mappings affect optimization performance, from the perspectives of invariant measures and traversal efficiency of ergodic chaotic motion. In comparative tests with 21 meta-heuristic algorithms on the CEC2014, CEC2019, and CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior optimization performance. Furthermore, the practicability and robustness of CEAHA have been confirmed in mechanical design optimization problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, and piston levers.

关键词： Meta-heuristic optimization Chaos artificial hummingbird algorithm Mechanical design optimization

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A low-carbon economic dispatch method for regional integrated energy system based on multi-objective chaotic artificial hummingbird algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Cao, Jie Yang, Yuanbo Qu, Nan Xi, Yang Guo, Xiaoli Dong, Yunchang Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China Nanjing Power Supply Co Jiangsu Elect Power Co Ltd Nanjing 210000 Peoples R China

This paper investigates Regional Integrated Energy Systems (RIES), emphasizing the connection of diverse energy supply subsystems to address varied user needs and enhance operational efficiency. A novel low-carbon economic dispatch method, utilizing the multi-objective chaotic artificial hummingbird algorithm, is introduced. The method not only optimizes economic and environmental benefits but also aligns with "carbon peak and carbon neutrality" objectives. The study begins by presenting a comprehensive low-carbon economic dispatch model, followed by the proposal of the multi-objective chaotic artificial hummingbird algorithm, crucial for deriving the Pareto frontier of the low-carbon economic dispatch model. Additionally, we introduce a TOPSIS approach based on combined subjective and objective weights, this approach harnesses the objective data from the Pareto solution set deftly, curbs the subjective biases of dispatchers effectively and facilitates the selection of an optimal system operation plan from the Pareto frontier. Finally, the simulation results highlight the outstanding performance of our method in terms of optimization outcomes, convergence efficiency, and solution diversity. Noteworthy among these results is an 8.8% decrease in system operational economic costs and a 14.2% reduction in carbon emissions.

关键词： Regional integrated energy system Multi-objective optimization Low-carbon economic dispatch artificial hummingbird algorithm

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