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AHA-3WKM: The optimization of K-means with three-way clustering and artificial hummingbird algorithm

INFORMATION SCIENCES 2024年 672卷

作者： Chen, Xiying Liu, Caihui Lin, Bowen Lai, Jianying Miao, Duoqian Gannan Normal Univ Dept Math & Comp Sci Ganzhou 34100 Jiangxi Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China

Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K -means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K -means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high -quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results.

关键词： K-means clustering artificial hummingbird algorithm Three-way clustering Cluster centers Fitness function

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

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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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A Modified artificial hummingbird algorithm for solving optimal power flow problem in power systems

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ENERGY REPORTS 2024年 11卷 982-1005页

作者： Ebeed, Mohamed Abdelmotaleb, Mohamed A. Khan, Noor Habib Jamal, Raheela Kamel, Salah Hussien, Abdelazim G. Zawbaa, Hossam M. Jurado, Francisco Sayed, Khairy Sohag Univ Fac Engn Dept Elect Engn Sohag 82524 Egypt Sohag Univ Fac Technol & Educ Elect Dept Sohag 82524 Egypt North China Elect Power Univ Dept New Energy Key Lab Wind Power Beijing Peoples R China North China Elect Power Univ Sch Water Resources & Hydro Power Engn Beijing Peoples R China Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Univ Jaen Dept Elect Engn EPS Linares Jaen 23700 Spain

Optimal power flow (OPF) problem solution is a crucial task for the operators and decision makers to assign the best setting of the system components to obtain the most economic, environmental, and technical suitable state. artificial hummingbird algorithm is a recent optimization algorithm that has been applied to solving several optimization problems. In this paper, a Modified artificial hummingbird algorithm (MAHA) is proposed for improving the performance of the orignal artificial hummingbird algorithm as well as effectivelly solve the OPF problem. The proposed MAHA is based on improving the searching capability by boosting the exploitation using the bandwidth motion around the best solution, while the exploration process is improved using the Levy flight distribution motion and the fitness-distance balance selection. This modified version helps overcome issues such as stagnation, premature convergence, and a propensity for local optima when tackling complex, nonlinear, and non-convex optimization problems like OPF. In order to confirm the effectiveness of the proposed algorithm, a series of tests are conducted on 23 standard benchmark functions, including CEC2020. The resulting outcomes are then compared to those obtained using other algorithms such as fitness-distance balance selection-based stochastic fractal search (FDBSFS), antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), fitness-distance balance and learning based artificial bee colony (FDB-TLABC), and traditional artificial hummingbird algorithm (AHA).The proposed algorithm is evaluated by solving the OPF problem with multiple objective functions on the IEEE 30-bus system. These objectives include fuel cost, fuel cost with valve loading effects, power losses, emissions, and voltage profile. Additionally, the algorithm's effectiveness is further assessed by testing it on single objective functions using medium and large-scale IEEE 57 and 118-bus *** results

关键词： artificial hummingbird algorithm Emission Levy Flight Distribution Optimal power flow Power losses

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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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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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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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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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Interval variational approach for production control and waste reduction using artificial hummingbird algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-32页

作者： Das, Subhajit Alrasheedi, Adel Fahad Shaikh, Ali Akbar Mirjalili, Seyedali Bhagalpur Coll Engn Sabour Dept Math Bhagalpur 813210 India King Saud Univ Coll Sci Dept Stat & Operat Res PO Box 2455 Riyadh 11451 Saudi Arabia Univ Burdwan Dept Math Burdwan 713104 India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

Nowadays, consumers show more interest towards eco-friendly products. To meet this demand, however, manufacturing processes often generate a lot of hazardous waste, which creates challenges for companies. To tackle these issues, this work develops an optimization model to help companies with managing production, reduce waste, and maintain green product standards. To navigate solution of the profit maximization problem became apparent in the model, a new meta-heuristics called artificial hummingbird algorithm is employed and compared with a wide range of other optimization techniques. The results demonstrate that this algorithm outperforms others on the majority of case studies. Sensitivity analyses are also performed to help managers make informed decisions.

关键词： Green production Waste-disposal investment Imperfect production Salvage Interval variational problem artificial hummingbird algorithm Optimization algorithm

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A levy chaotic horizontal vertical crossover based artificial hummingbird algorithm for precise PEMFC parameter estimation

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Jangir, Pradeep Ezugwu, Absalom E. Saleem, Kashif Arpita Agrawal, Sunilkumar P. Pandya, Sundaram B. Parmar, Anil Gulothungan, G. Abualigah, Laith Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Graph Era Hill Univ Dept CSE Dehra Dun 248002 Uttar Pradesh India Graph Era Dept CSE Dehra Dun 248002 Uttarakhand India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan North West Univ Unit Data Sci & Comp 11 Hofman St ZA-2520 Potchefstroom South Africa King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh 11362 Saudi Arabia Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 Tamil Nadu India Govt Engn Coll Dept Elect Engn Gandhinagar 382028 Gujarat India Shri KJ Polytech Dept Elect Engn Bharuch 392001 India SRM Inst Sci & Technol Dept Elect & Commun Engn Chengalpattu 603203 Tamil Nadu India Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Jadara Univ Res Ctr POB 733 Irbid Jordan

In this research, enhanced versions of the artificial hummingbird algorithm are used to accurately identify unknown parameters in Proton Exchange Membrane Fuel Cell (PEMFC) models. In particular, we propose a multi strategy variant, the L & eacute;vy Chaotic artificial hummingbird algorithm (LCAHA), which combines sinusoidal chaotic mapping, L & eacute;vy flights and a new cross update foraging strategy. The combination of this method with PEMFC parameters results in a significantly improved performance compared to traditional methods, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Sparrow Search algorithm (SSA), which we use as baselines to validate PEMFC parameters. The quantitative results demonstrate that LCAHA attains a minimum Sum of Squared Errors (SSE) of 0.0254 and standard deviation of 4.59E-08 for the BCS 500W PEMFC model, which is much lower than the SSE values obtained for PSO (0.1924) and GWO (0.0364), thereby validating the superior accuracy and stability of LCAHA. Moreover, LCAHA converges faster than DE and SSA, reducing runtime by about 47%. The robustness and reliability of LCAHA-simulated and actual I-V curves across six PEMFC stacks are shown to be in close alignment.

关键词： PEM fuel cell Optimal parameter estimation Electrical Engineering Optimization artificial hummingbird algorithm LCAHA

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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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