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bald eagle search algorithm based optimal reconfiguration of centralized Thermoelectric Generation System under non-uniform temperature distribution condition

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FRONTIERS IN ENERGY REsearch 2022年 10卷

作者： Zhang, Minhao Fang, Xia Sichuan Univ Sch Mech Engn Chengdu Peoples R China

Fossil energy is becoming increasingly scarce, and technological innovation to promote clean energy consumption and achieve the "dual carbon " goal has increasingly become the focus of discussion. Compared with the full coverage thermoelectric module design scheme, the optimized layout scheme uses fewer thermoelectric generation (TEG) modules, thus confirming that the more TEG modules that are not arranged, the better. This research provides a possible way to improve the output power of TEG system. This paper proposed a bald eagle search algorithm (BES) scheme to optimize centralized TEG array reconfigures, which has not been previously employed to achieve a fast and effective tracking of the maximum power point tracking (MPPT) under non-uniform temperature difference (NTD) condition. In order to efficiently seek the global MPP (GMPP) under NTD condition, a BES algorithm is adopted to TEG reconfigures arrays to evidently improve the global searching ability of BES algorithm through the previous searching results. Furthermore, the main method in this paper is preliminarily verified on MATLAB. Both simulation and experimental results show that BES algorithm can significantly improve the convergence accuracy and output power.

关键词： maximum power point tracking non-uniform temperature distribution TEG array reconfiguration bald eagle search algorithm global maximum power point tracking

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bald eagle search algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell

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FRONTIERS IN ENERGY REsearch 2022年 10卷

作者： Yang, Bo Li, Danyang Zeng, Chunyuan Han, Yiming Li, Junhui Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China Faculty of Electric Power Engineering Kunming University of Science and Technology Kunming China

A precise and reliable proton exchange membrane fuel cell (PEMFC) parameter identification performs an essential function in simulation analysis, optimal control, and performance research of actual PEMFC systems. Unfortunately, achieving an accurate, efficient, and stable parameter identification can sometimes be problematic for traditional optimization methods, owing to its strong coupling, inherent nonlinear, and multi-variable characteristics. Therefore, an advanced bald eagle search (BES) algorithm is designed to dependably identify the unknown parameters of the electrochemical PEMFC model in this work. For evaluating and analyzing the overall optimization performance of the BES comprehensively, it is compared with the genetic algorithm (GA) based on MATLAB under three cases. According to the simulation results, the optimum root mean square error (RMSE) achieved by BES is 96.27% less than that achieved by GA in parameter identification, which fully indicates that the precision, accuracy, and stability of the optimization results can be remarkably improved via the application of BES.

关键词： proton exchange membrane fuel cell parameter identification bald eagle search algorithm metaheuristic algorithm MATLAB

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Fuel Cell Electric Vehicle Hydrogen Consumption and Battery Cycle Optimization Using bald eagle search algorithm

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7744页

作者： Savran, Efe Karpat, Esin Karpat, Fatih Bursa Uludag Univ Dept Mech Engn TR-16059 Bursa Turkiye Bursa Uludag Univ Dept Elect Elect Engn TR-16059 Bursa Turkiye

In this study, the bald eagle search algorithm performed hydrogen consumption and battery cycle optimization of a fuel cell electric vehicle. To save time and cost, the digital vehicle model created in Matlab/Simulink and validated with real-world driving data is the main platform of the optimization study. The digital vehicle model was run with the minimum and maximum battery charge states determined by the bald eagle search algorithm, and hydrogen consumption and battery cycle values were obtained. By using the algorithm and digital vehicle model together, hydrogen consumption was minimized and range was increased. It was aimed to extend the life of the parts by considering the battery cycle. At the same time, the number of battery packs was included in the optimization and its effect on consumption was investigated. According to the study results, the total hydrogen consumption of the fuel cell electric vehicle decreased by 57.8% in the hybrid driving condition, 23.3% with two battery packs, and 36.27% with three battery packs in the constant speed driving condition.

关键词： fuel cell electric vehicle optimization bald eagle search algorithm hydrogen consumption battery life

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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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Multi-Reservoir Flood Control Operation Using Improved bald eagle search algorithm with ε Constraint Method

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WATER 2023年第4期15卷 692页

作者： Wang, Wenchuan Tian, Weican Chau, Kwokwing Zang, Hongfei Ma, Mingwei Feng, Zhongkai Xu, Dongmei North China Univ Water Resources & Elect Power Coll Water Resources Henan Key Lab Water Resources Conservat & Intens U Zhengzhou 450046 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210024 Peoples R China

The reservoir flood control operation problem has the characteristics of multiconstraint, high-dimension, nonlinearity, and being difficult to solve. In order to better solve this problem, this paper proposes an improved bald eagle search algorithm (CABES) coupled with epsilon-constraint method (epsilon-CABES). In order to test the performance of the CABES algorithm, a typical test function is used to simulate and verify CABES. The results are compared with the bald eagle algorithm and particle swarm optimization algorithm to verify its superiority. In order to further test the rationality and effectiveness of the CABES method, two single reservoirs and a multi-reservoir system are selected for flood control operation, and the epsilon constraint method and the penalty function method (CF-CABES) are compared, respectively. Results show that peak clipping rates of epsilon-CABES and CF-CABES are both 60.28% for Shafan Reservoir and 52.03% for Dahuofang Reservoir, respectively. When solving the multi-reservoir joint flood control operation system, only epsilon-CABES flood control operation is successful, and the peak clipping rate is 51.76%. Therefore, in the single-reservoir flood control operation, the penalty function method and the epsilon constraint method have similar effects. However, in multi-reservoir operation, the epsilon constraint method is better than the penalty function method. In summary, the epsilon-CABES algorithm is more reliable and effective, which provides a new method for solving the joint flood control scheduling problem of large reservoirs.

关键词： flood control operation bald eagle search algorithm multi-reservoir epsilon constraint method penalty function method

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Research on Multi-objective Optimization of Freight Train Operation Process Based on Improved bald eagle search algorithm

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电脑学刊 2022年第5期33卷 135-150页

作者： Lingzhi Yi Dake Zhang Wang Li Renzhe Duan Peng Jiang Bo Liu

To study the optimization problem of freight train operation process under complex line conditions, this paper adopts the freight train multi-particle model to establish the multi-objective optimization model of the freight train. Aiming at the problem of difficult optimization caused by complex line conditions, a two-step method is used to find the optimal operation strategy of the freight train. Firstly, the greedy algorithm is used to select the optimal working condition sequence. Secondly, the multi-objective optimization algorithm is used to obtain the position of the ideal working condition transition point. By introducing the idea of non-dominated sorting, an improved multi-objective bald eagle search algorithm is proposed to optimize the operation process of freight trains. This algorithm adopts the evolution method of combining bald eagle population renewal with adaptive Gaussian mutation, and introduces preference information to increase the rationality of population evolution. The simulation results show that the optimal operation strategy of freight train selected by two-step method is in line with the actual operation situation, and the proposed multi-objective bald eagle search algorithm considers both convergence and distribution. The results can satisfy the preference of decision-makers, which can provide a reference target speed curve for railway workers.

关键词： freight train multi-particle model multi-objective optimization bald eagle search algorithm preference information target speed curve

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A Hybrid bald eagle-Crow search algorithm for Gaussian mixture model optimisation in the speaker verification framework

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Decision Analytics Journal 2024年 10卷

作者： Nirmal, Asmita Jayaswal, Deepak Kachare, Pramod H. Department Electronics and Telecommunication Engineering Datta Meghe College of Engineering Airoli Maharashtra Navi Mumbai 400708 India Department Electronics and Telecommunication Engineering St. Francis Institute of Technology Borivali Maharashtra Mumbai 400103 India Department Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology D.Y. Patil Deemded to be University Nerul Maharashtra Navi Mumbai 400706 India

A mutualism of nature-inspired algorithms, the bald eagle search algorithm (BESA) with the Crow search algorithm (CSA), is proposed for Gaussian Mixture Model optimisation in the speaker verification framework. BESA simulates bald eagles’ hunting behaviour, whereas CSA simulates crows’ food-hiding behaviour. BESA excels in exploitation but has limitations in exploration, whereas CSA showcases strong exploration capabilities but faces challenges in effective exploitation. BESA's hunting characteristic combines crows’ memory update to generate a Hybrid bald eagle-Crow search algorithm (HBE-CSA). The ability of the proposed HBE-CSA is tested on clean speech utterances from the Librispeech dataset and eight real-life noisy conditions from the AURORA dataset at four different Signal-to-Noise Ratios (SNRs). The comparative analysis shows that the proposed HBE-CSA converges with higher log-likelihood than individual search algorithms and the state-of-the-art Expectation Maximisation algorithm. Detection error trade-off curves validate the results of convergence behaviour, indicating that best speaker models are built using the proposed HBE-CSA. The noise analysis shows the smallest equal error rate for the proposed HBE-CSA at different SNRs than competing algorithms. The proposed HBE-CSA holds promise for optimising models not only in speaker verification but also across other areas of speech processing. © 2023 The Author(s)

关键词： bald eagle search algorithm Crow search algorithm Gaussian mixture model Hybrid optimisation algorithm Speaker verification

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A new approach for fire and non-fire aerosols discrimination based on multilayer perceptron trained by modified bonobo optimizer

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-33页

作者： Zheng, Rong Li, Genliang Li, Ruikang Che, Yan Wen, Hui Lu, Song Putian Univ New Engn Ind Coll 1133 Xueyuan Rd Putian 351100 Fujian Peoples R China Sanming Univ Sch Informat Engn 25 Jindong Rd Sanming 365004 Fujian Peoples R China Putian Univ Sch Mech Elect & Informat Engn 1133 Xueyuan Rd Putian 351100 Fujian Peoples R China Univ Sci & Technol China State Key Lab Fire Sci 96 JinZhai Rd Hefei 230027 Anhui Peoples R China

Fire smoke detection plays a pivotal role in our life. For the optical fire smoke detector, it is easy to produce false alarms due to the misidentification of non-fire aerosols, such as dust and water mist. In this research, four multilayer perceptron (MLP) models are established and applied to discriminate the fire smokes and non-fire aerosols. A new hybrid algorithm (HBOBES) is proposed to optimize the parameters of MLP, which incorporates three search phases, including space selecting, promiscuous and restrictive mating, and consortship and extra-group mating, which are derived from the basic bonobo optimizer (BO) and bald eagle search algorithm (BES). Moreover, a adaptive tent chaos mapping technique is introduced into the first two phases to increase the population diversity. In the experiments, eight standard classification datasets and sixteen self-established aerosol classification datasets are applied to evaluate HBOBES's optimization performance on training the MLP models. The results show that the proposed HBOBES ranks first overall, showing the merits in training MLP models compared to eight other algorithms. For the aerosol classification problem, it is found that both the optimized 3-7-2 and 3-7-3 MLP models have achieved the highest classification accuracy of approximately 95%. Black smokes can be identified with 100% classification accuracy. Therefore, this paper provides an effective and feasible approach to distinguish fire and non-fire aerosols by using the MLP classifier optimized by optimization algorithm, which has practical significance for the development of intelligent optical smoke detector.

关键词： Multilayer perceptron Bonobo optimizer bald eagle search algorithm Fire smoke detection Aerosols classification

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Dual Feature-Based Intrusion Detection System for IoT Network Security

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-19页

作者： Biju, A. Franklin, S. Wilfred Maria Coll Engn & Technol Dept Comp Sci & Engn Attoor Tamil Nadu India CSI Inst Technol Dept Elect & Commun Engn Nagercoil Tamil Nadu India

The Internet of Things (IoT) has enabled widespread connectivity of smart devices but remains susceptible to cyber intrusions. In this research, a novel dual feature optimization using deep learning network for intrusion Detection (FOUND) technique has been proposed for enhancing security in IoT environments. The proposed method utilizes the bald eagle search (BES) algorithm and butterfly optimization algorithm (BOA) to capture both flow and packet level features to enhance the accuracy of the intrusion detection process. Moreover, a multi-head attention-based bidirectional gated recurrent unit (MHA-BiGRU) is utilized to classify Attack and Non-Attack classes with high precision. The efficacy of the suggested approach is measured utilizing metrics including recall (RC), accuracy, precision (PR), and f1score (F1S). Experimental outcomes utilizing BoT-IoT and UNSW-NB15 datasets demonstrate greater accuracy over existing models. In BoT-IoT, the accuracy of the FOUND approach is 1.5%, 1.1%, and 2.5% increase compared to existing GRU, RNN, and GCN methods, respectively.

关键词： Internet of things bald eagle search algorithm Deep learning Intrusion detection Bidirectional gated recurrent unit

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A hybrid model of bald eagle search and relevance vector machine for dam safety monitoring using long-term temperature

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ADVANCED ENGINEERING INFORMATICS 2023年 55卷

作者： Yu, Xin Li, Junjie Kang, Fei Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Dalian 116024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Accurate simulation of temperature effect is a major challenge for computational (intelligent) prediction models used for monitoring health of high concrete dams, especially in regions with long freezing periods and distinct seasons, occasional extreme weather. A Hydrostatic-Temperature long-term-Time (HTLT) model was proposed for better temperature effect simulation using long-term measured environment temperatures and explored the influence of temperatures data sets of different time lengths on dam displacement prediction accuracy with the traditional Hydrostatic-Season-Time model as control. The bald eagle search algorithm was coupled with the Relevance Vector Machine to form a novel hybrid model (BES-RVM) for predicting concrete gravity dam displacement response and comparison of nonlinear mapping capability between different kernel functions was conducted. Further optimized by Successive Projections algorithm (SPA) for feature selection, sensitive features were selected from the proposed HTLT variable sets to improve the prediction model's accuracy. The prediction model was experimented on a real concrete dam with results showing that the BES-RVM model gave excellent prediction performance. Using the HTLT variable sets significantly reduced the prediction errors and the best performed result came from variables of the two-year long temperatures data. The SPA optimized BES-RVM model largely brought down the dimension and the collinearity of the input variables and further improved the prediction performance.

关键词： Structural health monitoring Concrete dam displacement prediction bald eagle search algorithm Relevance vector machine regression Successive projections algorithm Temperature simulation

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