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Global information enhanced adaptive bald eagle search algorithm for photovoltaic system optimization

ENERGY REPORTS 2025年 13卷 2129-2152页

作者： Guo, Wenyan Hou, Zhuolin Dai, Fang Wang, Junfeng Li, Shenglong Xian Univ Technol Xian Peoples R China

The parameter estimation of solar photovoltaic system (SPVS) models presents a formidable optimization challenge. The crux of resolving this issue lies in the efficacy of intelligent algorithms. By addressing the parameter optimization challenges associated with three photovoltaic models and targeting the deficiencies of the bald eagle search optimization algorithm (BES), such as its limited local search capability, inadequate diversity, and suboptimal solution performance, this article introduces a novel approach: the virtual-particle adaptive bald eagle search optimization algorithm (VABES). Incorporated into VABES is a virtual particle updating mechanism, leveraging comprehensive population information to enhance the prospects of individuals converging towards the most desired solution, while simultaneously preventing the population from becoming trapped at extreme points. To strike a balance between aggregation and dispersion within the swarm and bolster VABES's global optimization prowess, an adaptive probability-guided Levy flight correction scheme has been devised. Additionally, a dynamic mutation factor-based mutation and crossover strategy has been formulated to refine VABES's exploitation capabilities. The results of 100-dimensional tests conducted on 40 benchmark functions from CEC2017 and CEC2020, coupled with nonparametric testing, reveal that VABES outperforms at least 66 % of other competitive improved algorithms. Experiments involving 57 real-world problems demonstrate that VABES's total performance metric stands at 0.1466, ranking it first and showcasing its notable advantages in handling constrained optimization compared to BES-related improved algorithms. Furthermore, the solution processes for three photovoltaic parameter optimization models illustrate that VABES converges rapidly, reducing errors by 26 %, and exhibits certain advantages in estimating diverse photovoltaic model parameters.

关键词： Photovoltaic model bald eagle search algorithm Virtual particles Adaptive probability Mutation and crossover Constrained optimization

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An Advanced bald eagle search algorithm for Image Enhancement

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Computers, Materials & Continua 2025年第3期82卷 4485-4501页

作者： Pei Hu Yibo Han Jeng-Shyang Pan School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced *** paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal *** our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and *** final enhanced image is obtained through color *** BES algorithm seeks the optimal solution through the selection,search,and swooping ***,it is prone to getting stuck in local optima and converges *** overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control *** is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective *** benchmark images are collected to validate its *** results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.

关键词： Image enhancement gamma correction retinex bald eagle search algorithm

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Switchgear Fault Diagnosis Based on Support Vector Machine Optimized by an Improved bald eagle search algorithm

Switchgear Fault Diagnosis Based on Support Vector Machine O...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Xu, Jianwen Wen, Jianbing Chen, Weizhuo Zhou, Dan Li, Qianyuan Haikou Power Supply Bureau of Hainan Power Grid Co. Ltd Hainan Haikou570100 China

ISBN: (纸本)9789819618552

Switchgear plays a crucial role as a fundamental element within the power system, its stable operation is essential to guarantee the dependability and security of the entire power grid. Due to the specificity and complexity of the working environment, switchgear is prone to various faults. To guarantee the consistent functioning of the switchgear, a switchgear fault diagnosis model based on the improved bald eagle search algorithm (BES) optimized Support Vector Machine (SVM) is proposed. Twelve sets of state data on switchgear operation were collected to create a fault diagnosis dataset, the bald eagle search algorithm is improved by adopting the opposition-based learning strategy and the adaptive weight adjustment strategy to enhance the algorithm’s optimization ability for the penalty factor c and the kernel function parameter g of the support vector machine. Case analysis shows that for the four types of switchgear operating states, the improved model achieves diagnosis accuracy of 97.44%, which realizes the accurate identification of switchgear fault diagnosis, and providing essential technical support for technicians to conduct switchgear maintenance. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： bald eagle search algorithm Fault diagnosis Support vector machine Switchgear

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Development of vanilla LSTM based stuttered speech recognition system using bald eagle search algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第8期17卷 4077-4086页

作者： Premalatha, S. Kumar, Vinit Jagini, Naga Padmaja Reddy, Gade Venkata Subba K S R Inst Engn & Technol Dept Elect & Commun Engn Tiruchengode 637215 Tamil Nadu India Galgotias Coll Engn & Technol Dept Comp Sci & Engn Greater Noida 201310 Uttar Pradesh India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad 501218 Telangana India Gokaraju Rangaraju Inst Engn & Technol Dept Elect & Commun Engn Hyderabad 500090 Telangana India

Stuttering or stammering is considered as the most important parameter in the speech recognition algorithm. For the conversion of stuttered speech into readable text, first it is vital to detect the stuttered speech. In many existing models, the exactness of the recognition system is degraded because of the additional noise present in the speech signals. Therefore, proposed a bald eagle search algorithm based vanilla long-short term memory (BES-vanilla LSTM), a system for identifying the stuttered speech among the number of speech signals. Initially, the dataset collected from the TORGO database undergoes preprocessing phase for the elimination of unwanted noise signals present in the input speech signals using the spectral subtraction method. Further, the preprocessed signals are passed to the feature extraction phase. In this phase, the pitch frequencies are extracted from the input signals. Then the extracted pitch frequencies are passed to the input layer of the Vanilla LSTM for stuttered speech recognition. Using the fitness value of the BES algorithm, the LSTM recognizes the stuttered speech and is given at the output layer. The proposed system is implemented in the python tool. The comparison of the suggested system's performance is made with the other existing system by metrics such as f1-score, recall, accuracy and precision, and the overall efficiency of the designed system is studied.

关键词： Spectral subtraction method bald eagle search algorithm Speech recognition Vanilla long-short term memory Feature extraction

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Robust parameter estimation approach of Lithium-ion batteries employing bald eagle search algorithm

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INTERNATIONAL JOURNAL OF ENERGY REsearch 2022年第8期46卷 10564-10575页

作者： Fathy, Ahmed Ferahtia, Seydali Rezk, Hegazy Yousri, Dalia Abdelkareem, Mohammad Ali Olabi, Abdul Ghani Jouf Univ Elect Engn Dept Fac Engn Sakaka Saudi Arabia Univ Msila Dept Elect Engn Lab Anal Signaux & Syst Msila Algeria Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Fayoum Univ Elect Engn Dept Fac Engn Al Fayyum Egypt Univ Sharjah Dept Sustainable & Renewable Energy Engn POB 27272 Sharjah U Arab Emirates

Lithium-ion batteries play an essential role in sustainable power systems as energy storage systems. They are employed for electrical power storage and balancing power in multisources power systems. However, due to the degradation phenomena, these systems are more exposed to parametric variation. For this reason, the identification of the battery parameters remains crucial to enhance the power system performance and extend the battery lifespan. A robust parameter identification strategy based on the bald eagle search algorithm (BES) is proposed in this research work. This article proposed a robust estimation approach that accurately estimates the battery parameters based on the BES optimizer. The proposed approach has been evaluated for two distinct batteries to confirm its superiority. A comparison with recent optimization methods has been performed to validate the performance of the proposed strategy. These algorithms include marine predator algorithm, COOT algorithm, artificial eco-system optimizer, and other previous algorithms. The obtained results affirmed the reliability of the proposed BES-based strategy to estimate the battery equivalent circuit variables with superior accuracy compared with other methods. It provided the smallest fitness function value for the first battery with 7.06 x 10(-4) and an excellent SD value of 1.877 x 10(-8). The proposed strategy achieved 100% efficiency in terms of optimization efficiency. The second battery has been tested with ArtUban and New European Driving Cycle (NEDC) profiles to confirm the performance of BES. The results confirm the superior performance for the two cases. The BES provides the smallest fitness values (0.0860 for ArtUrban and 0.1222 for NEDC) and lower identification total error (2.294 for ArtUrban and 2.6737 for NEDC).

关键词： bald eagle search algorithm energy storage Lithium-ion batteries parameter estimation

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An Improved bald eagle search algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1603-1642页

作者： Wenchuan Wang Weican Tian Kwok-wing Chau Yiming Xue Lei Xu Hongfei Zang College of Water Resources Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River BasinNorth China University of Water Resources and Electric PowerZhengzhou450046China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong KongChina College of Hydrology and Water Resources Hohai UniversityNanjing210024China

The bald eagle search algorithm(BES)is an emerging meta-heuristic *** algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping ***,BES tends to drop-in local optimization and the maximum value of search space needs to be *** fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local ***,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search ***,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of *** verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the *** findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization algorithm(WOA)and Archimedes algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing ***,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

关键词： bald eagle search algorithm cauchymutation adaptive weight factor CEC2017 benchmark functions engineering optimization problems

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Adaptive bald eagle search algorithm with elite swarm guiding and population memory crossover

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第3期24卷 133-149页

作者： Zhang, Dongmei Liang, Xiaolei Cao, Yulian Zhang, Mengdi Xiao, Di Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan Peoples R China Beijing Univ Posts & Telecommun Sch Modern Post Beijing 100876 Peoples R China

bald eagle search (BES) algorithm is easy to fall into local optimums and the population diversity declines rapidly. To address the problems, an adaptive bald eagle search (ABES) algorithm with elite swarm guiding and population memory crossover is proposed. At the selecting stage, an elite population information is provided to replace a single information to guide location updating. At the searching stage, a population memory crossover strategy is proposed. The swooping stage is deleted in ABES due to search redundancy. Furthermore, the key parameters of ABES are designed to adjust adaptively to improve the population search ability. Finally, the test suite of CEC-2013 and three real-world engineering design problems are taken to test the performance of ABES, compared with seven state-of-art algorithms. The experimental results show that ABES has a much better comprehensive performance on convergence and local search capability than other selected algorithms.

关键词： bald eagle search algorithm elite swarm guidance population memory crossover recombination adaptive

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Multi-Strategy bald eagle search algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization

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SENSORS 2024年第21期24卷 6794页

作者： Niu, Haixu Li, Yonghai Zhang, Chunyu Chen, Tianfei Sun, Lijun Abdullah, Muhammad Irsyad Management & Sci Univ Fac Informat Sci & Engn Shah Alam 40100 Malaysia Henan Univ Technol Sch Management Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including L & eacute;vy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm's robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.

关键词： wireless sensor network coverage control bald eagle search algorithm L & eacute vy flight quasi-reflection-based learning quadratic interpolation orthogonal learning

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A multi-strategy boosted bald eagle search algorithm for global optimization and constrained engineering problems: case study on MLP classification problems

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第1期58卷 1-50页

作者： Zheng, Rong Li, Ruikang Hussien, Abdelazim G. Hamad, Qusay Shihab Al-Betar, Mohammed Azmi Che, Yan Wen, Hui Putian Univ New Engn Ind Coll Putian 351100 Fujian Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Fujian Peoples R China Putian Univ Sch Mech Elect & Informat Engn Putian 351100 Fujian Peoples R China Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan Univ Informat Technol & Commun UOITC Baghdad Iraq Ajman Univ Coll Engn & Informat Technol Dept Informat Technol POB 346 Ajman U Arab Emirates Ajman Univ Artificial Intelligence Res Ctr POB 346 Ajman U Arab Emirates

The bald eagle search (BES) algorithm is an innovative population-based method inspired by the intelligent hunting behavior of bald eagles. While BES shows promise, it faces challenges such as susceptibility to local optima and imbalances between exploration and exploitation phases. To address these limitations, this paper introduces the Multi-Strategy Boosted bald eagle search (MBBES) algorithm. MBBES enhances the original BES by incorporating an adaptive parameter, two distinct mutation strategies, and replacing the swoop stage with a fall stage. We rigorously evaluate MBBES against classic and improved algorithms using the CEC2014 and CEC2017 test sets. The experimental results demonstrate that MBBES significantly improves the ability to escape local optima and achieves superior convergence accuracy. Moreover, MBBES ranks first according to the Friedman test, outperforming its counterparts in solving five practical engineering problems and three MLP classification problems, underscoring its effectiveness in real-world optimization scenarios. These findings indicate that MBBES not only surpasses BES but also sets a new benchmark in optimization performance.

关键词： bald eagle search algorithm Mutation strategy Multi-layer perceptron Global optimization problem Metaheuristic algorithm

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Soil moisture content prediction model for tea plantations based on SVM optimised by the bald eagle search algorithm

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COGNITIVE COMPUTATION AND SYSTEMS 2021年第4期3卷 351-360页

作者： Huang, Ying Jiang, Hao Wang, Wen-feng Wang, Weixing Sun, Daozong Wuhan Univ Elect Informat Sch Wuhan 430072 Hubei Peoples R China Liuzhou Railway Vocat Tech Coll Liuzhou Peoples R China Shanghai Inst Technol Sch Elect & Elect Engn Haiquan Rd 100 Shanghai Peoples R China South China Agr Univ Sch Elect Engn Guangzhou Peoples R China

In order to solve the problem of low accuracy and efficiency of soil moisture content prediction in tea plantations and improve the level of soil water content prediction, a soil moisture content prediction model for tea plantations based on the support vector machine (SVM)-optimised bald eagle search (BES) algorithm (BES-SVM) is proposed. Soil data and environmental data of tea plantations were transmitted to the server using sensor nodes and weather station nodes. The prediction models of soil moisture content and natural environmental parameters such as soil electrical conductivity, soil temperature, air temperature, air humidity, light intensity, and rainfall were developed using the SVM model optimised by the bald eagle search algorithm, and the mean square error (MSE) and coefficient of determination (R2) were calculated to evaluate the model performance. Meanwhile, the performance of the BES-SVM model is compared with the particle swarm algorithm optimisation SVM (PSO-SVM) and genetic algorithm optimised SVM (GA-SVM) models. Results show that the proposed model has a mean coefficient of determination of 95.65%, and the prediction performance is better than the PSO-SVM and GA-SVM model, indicating that the BES-SVM model has good performance and is a feasible prediction method for soil water content prediction and guiding irrigation and fertilisation management in tea plantations.

关键词： bald eagle search algorithm precision irrigation soil moisture content support vector machine

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