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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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Modified Elite Opposition-Based artificial hummingbird algorithm for Designing FOPID Controlled Cruise Control System

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Intelligent Automation & Soft Computing 2023年第11期38卷 169-183页

作者： Laith Abualigah Serdar Ekinci Davut Izci Raed Abu Zitar Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Computer Engineering Batman UniversityBatman72100Turkey MEU Research Unit Middle East UniversityAmmanJordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and *** study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter *** approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and *** study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and *** highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous *** study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe *** research provides a promising avenue for further research and development in this area.

关键词： Cruise control system FOPID controller artificial hummingbird algorithm elite opposition-based learning

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Modified Bow-Tie Antenna Design Using artificial hummingbird algorithm for Wireless Power Transfer IoT Applications 17

Modified Bow-Tie Antenna Design Using Artificial Hummingbird...

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17th European Conference on Antennas and Propagation (EuCAP)

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Griva, Aikaterini I. Rekkas, Vasileios P. Iliadis, Lazaros A. Sotiroudis, Sotirios P. Diamantoulakis, Panagiotis Lagkas, Thomas Sarigiannidis, Panagiotis Goudos, Sotirios K. Christodoulou, Christos Karagiannidis, George K. Aristotle Univ Thessaloniki Sch Phys ELEDIA AUTH Thessaloniki Greece Int Hellen Univ Dept Informat & Elect Engn ELEDIA AUTH Sindos Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece Int Hellen Univ Dept Comp Sci Kavala Greece Univ Western Macedonia Dept Informat & Telecommun Engn Kozani Greece Univ New Mexico Dept Engn & Comp Albuquerque NM USA

ISBN: (纸本)9788831299077

The advent of the Internet of Things has allowed the re-usability of established techniques combined with emerging technologies. In the TERMINET project, several key-enabling technologies in an IoT ecosystem are explored. The smart farming paradigm is one of the realistic use cases of the project, in which Wireless Power Transfer, assisted by an Unmanned Aerial Vehicle, will be demonstrated as a proof-of-concept, to deliver energy in wireless sensor networks. In this work, we provide a feasible solution of a rectifying antenna module in a rectenna system, which operates in the Wi-Fi 2.4 GHz frequency band. The proposed antenna is optimized by utilizing the artificial hummingbird algorithm. Numerical results demonstrate excellent performance of the proposed antenna, in terms of the key characteristics of reflection coefficient, input impedance, realized gain, and efficiency.

关键词： wireless power transfer radio frequency energy harvesting bow-tie antenna artificial hummingbird algorithm swarm intelligence optimization method Internet of Things

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Research on the Economic Scheduling Problem of Cogeneration Based on the Improved artificial hummingbird algorithm

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ENERGIES 2024年第24期17卷 6411-6411页

作者： Kong, Xiaohong Li, Kunyan Zhang, Yihang Tian, Guocai Dong, Ning Henan Inst Sci & Technol Sch Mech & Elect Engn Xinxiang 453003 Peoples R China

With the increasing application of Combined Heat and Power (CHP) units, Combined Heat and Power Economic Dispatch (CHPED) has emerged as a significant issue in power system operations. To address the complex CHPED problem, this paper proposes an effective economic dispatch method based on the Improved artificial hummingbird algorithm (IAHA). Given the complex constraints of the CHPED problem and the presence of valve point effects and prohibited operating zones, it requires the algorithm to have high traversal capability in the solution space and be resistant to becoming trapped in local optima. IAHA has introduced two key improvements based on the characteristics of the CHPED problem and the shortcomings of the standard artificial hummingbird algorithm (AHA). Firstly, IAHA uses chaotic mapping to initialize the initial population, enhancing the algorithm's traversal capability. Second, the guided foraging of the standard AHA has been modified to enhance the algorithm's ability to escape from local optima. Simulation experiments were conducted on CHP systems at three different scales: 7 units, 24 units, and 48 units. Compared to other algorithms reported in the literature, the IAHA algorithm reduces the cost in the three testing systems by up to USD 18.04, 232.7894, and 870.7461. Compared to other swarm intelligence algorithms reported in the literature, the IAHA algorithm demonstrates significant advantages in terms of convergence accuracy and convergence speed. These results confirm that the IAHA algorithm is effective in solving the CHPED problem while overcoming the limitations of the standard AHA.

关键词： Combined Heat and Power artificial hummingbird algorithm chaotic mapping intelligent algorithm economic dispatch

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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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LCAHA: A hybrid artificial hummingbird algorithm with multi-strategy for engineering applications

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023年第1期415卷

作者： Hu, Gang Zhong, Jingyu Zhao, Congyao Wei, Guo Chang, Ching-Ter Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Univ North Carolina Pembroke Pembroke NC 28372 USA Chang Gung Univ Dept Informat Management Taoyuan Taiwan

The recently introduced artificial hummingbird algorithm (AHA) exhibits competitive performance in developing optimization concerns. However, AHA has an imbalance between exploration and utilization abilities, often prematurely converging with low precision. Therefore, in this paper, a multi-strategy boosted hybrid artificial hummingbird algorithm called LCAHA combined with sinusoidal chaotic map strategy, Levy flight, cross, and update foraging strategy is proposed. Firstly, LCAHA is initialized by the sinusoidal chaotic map strategy to obtain a population with better ergodicity. Secondly, introducing the Levy flight can boost the diversity of the population, control premature convergence and boost the stability of the population. Then, two new strategies, cross foraging and update foraging, are introduced. The introduction of new foraging strategies further enhances the exploration and developmental capabilities. These three strategies work together to improve the overall performance of the AHA. Finally, the performance of the LCAHA is examined on 23 classical test suites, the CEC2017, CEC2019, and CEC2020 test suites, and six engineering optimization cases. The numerical experimental results show that LCAHA provides very promising numerical results in solving challenging optimization problems. The algorithm is applied to two spacecraft trajectory optimization cases. The experimental results demonstrate the applicability and potential of the LCAHA in solving practical applications.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： artificial hummingbird algorithm Engineering optimization Sinusoidal chaotic map Levy flight Cross and update foraging strategy Benchmark

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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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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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