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improved bat algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

Computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an improved bat algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,improved bat algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Energy-saving optimization of air-conditioning water system based on machine learning and improved bat algorithm

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ENERGY AND BUILDINGS 2025年 332卷

作者： Bai, Yan Sun, Di Liu, Liang Shen, Yifan Yao, Haitong Xian Univ Architecture & Technol Sch Sci 13 Yanta Rd Xian Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China Anhui Jianzhu Univ Anhui Prov Key Lab Intelligent Bldg Energy Saving Hefei 230022 Peoples R China

A significant portion of the total energy consumption in buildings comes from the air-conditioning water systems (ACWSs) used for cooling. The setpoints of ACWSs are usually determined based on experience or rules to meet cooling demands, but they may not be optimal under dynamic operating conditions. Therefore, to reduce energy consumption by dynamically adjusting the setpoints of ACWS, an energy-saving method of ACWS based on machine learning and improved bat algorithm (IBA) is proposed. Firstly, machine learning techniques are employed to develop models for component-level and system-level energy consumption, as well as chilled water and cooling water return temperature in the ACWS. Their hyperparameters are optimized by ten-fold crossvalidation and grid search. Secondly, the bat algorithm (BA) is improved by combining shared grouping strategy, nonlinear factor, and Levy flight strategy with adjustable balancing factor. Thirdly, the developed optimal energy consumption model is used as a fitness calculation function for the IBA, while the developed optimal return water temperature model is employed to predict return water temperature of the constrain conditions in the process of optimization. Finally, a case study was executed using a measured dataset of an ACWS. The results illustration that the energy consumption model based on the system-level has higher accuracy, robustness, and generalization ability than based on the component-level, and the total energy consumption of the system is reduced by 7.84% after optimization. Meanwhile, compared with the other optimization algorithms, the IBA has better optimization results with significant advantages in robustness, iteration times, global search capability, and computation time.

关键词： Air-conditioning water system Energy-saving optimization Machine learning improved bat algorithm

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Optimal Scheduling Method of Combined Wind-Photovoltaic-Pumped Storage System Based on improved bat algorithm

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PROCESSES 2025年第1期13卷

作者： Fan, Hui Wu, Hongbo Li, Shilin Han, Shengfeng Ren, Jingtao Huang, Shuo Zou, Hongbo State Grid Hebei Elect Power Co Ltd Shijiazhuang 050081 Peoples R China State Grid Xingtai Power Supply Co Ltd Xingtai 054001 Peoples R China Wuhan Univ Hubei Engn & Technol Res Ctr AC DC Intelligent Dis Sch Elect Engn & Automat Wuhan 430072 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

Pumped storage power stations not only serve as a special power load but also store excess electricity from the power system, significantly reducing the curtailment of wind and solar power. This dual function ensures the stable operation of the power grid and enhances its economic benefits. The scheduling optimization problem of a combined wind-solar-pumped storage system is addressed in this study, and an optimization scheduling model is proposed with the objective of maximizing total system revenue. The model is designed to comprehensively account for the generation revenues from wind power, photovoltaic power, thermal power, and pumped storage, as well as the penalty costs associated with pollutant emissions. To address the limitations of traditional algorithms, which are prone to being trapped in local optima and exhibit slow convergence, an improved bat algorithm was developed. The algorithm is enhanced through the use of chaotic mapping to expand the initial solution space, the incorporation of adaptive step-size updates to improve convergence efficiency, and the integration of the Cauchy function to strengthen global search capabilities, thereby effectively avoiding local optima. Simulation results have demonstrated that the improved algorithm achieves significant improvements over traditional bat algorithms and particle swarm optimization (PSO) in terms of optimization efficiency, with total revenue increases of 21.9% and 24.6%, respectively. The optimized scheduling plan is shown to fully utilize the flexible regulation capabilities of pumped storage, mitigating the adverse effects of wind and photovoltaic output fluctuations on grid operations and achieving a balanced trade-off between economic and environmental objectives.

关键词： renewable energy systems wind-photovoltaic-pumped storage optimal scheduling method improved bat algorithm

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improved bat algorithm for UAV Path Planning in Three-Dimensional Space

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IEEE ACCESS 2021年 9卷 20100-20116页

作者： Zhou, Xianjin Gao, Fei Fang, Xi Lan, Zehong Wuhan Univ Technol Sch Sci Wuhan 43007 Peoples R China Wuhan Univ Sch Econ & Management Wuhan 430072 Peoples R China

This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.

关键词： Path planning Optimization Heuristic algorithms Planning Convergence Radar Particle swarm optimization battlefield environment path planning improved bat algorithm convergence local search

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State of health estimation for lithium-ion batteries based on improved bat algorithm optimization kernel extreme learning machine

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JOURNAL OF ENERGY STORAGE 2024年 101卷

作者： Li, Xiangbin Fan, Diqing Liu, Xintian Xu, Shen Huang, Bixiong Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai Peoples R China

The state of health (SOH) estimation of lithium-ion batteries is crucial for ensuring the safety and stability of battery usage. An improved algorithm, combining the kernel extreme learning machine (KELM) and the improved bat algorithm (IBA), is proposed to estimate the SOH. Health indicators (HIs) are extracted from two public datasets, and their correlation with SOH is analyzed using the Pearson correlation coefficient. Principal component analysis (PCA) helps eliminate redundancy among the HIs input to the model. Following this, KELM is introduced. The swarm intelligence optimization algorithm, IBA, optimizes KELM's kernel function parameters and regularization coefficients. The retrieved HIs drive the model, and the robustness and accuracy of the outcomes are verified.

关键词： Lithium-ion batteries State of health improved bat algorithm Kernel extreme learning machine

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AERO-ENGINE EXHAUST GAS TEMPERATURE PREDICTION BASED ON LightGBM OPTIMIZED BY improved bat algorithm

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THERMAL SCIENCE 2021年第2期25卷 845-858页

作者： Li, Dingzhe Peng, Jingbo He, Dawei Air Force Engn Univ Aeronaut Engn Coll Xian Peoples R China

In this paper, an aero-engine exhaust gas temperature prediction model based on LightGBM optimized by the chaotic rate bat algorithm is proposed to monitor aero-engine performance effectively. By introducing chaotic rate, the convergence speed and precision of bat algorithm are improved, which chaotic rate hat algorithm is obtained. The LightGBM is optimized by chaotic rate bat algorithm and it is used to predict exhaust gas temperature. Taking a type of aero-engine for example, some relevant performance parameters from the flight data measured by airborne sensors were selected as input variables and exhaust gas temperature as output variables. The data set is divided into training and test sets, and the CRBA-LightGBM model is trained and tested, and compared with ensemble algorithms such as RF, XGBoost, GBDT, LightGBM, and BA-LightGBM. The results show that the mean absolute error of this method in the prediction of exhaust gas temperature (after normalization) is 0.0065, the mean absolute percentage error is 0.77% and goodness of fit R-2 has reached to 0.9469. The prediction effect of CRBA-LightGBM is better than other comparison algorithms and it is suitable for aero-engine condition monitoring.

关键词： aero-engine exhaust gas temperature prediction LightGBM improved bat algorithm flight data

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Synthesis of linear symmetric antenna arrays using improved bat algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2020年第6期62卷 2383-2389页

作者： Pan, Yusheng Zhang, Jiao Shanxi Univ Sch Phys & Elect Engn Taiyuan 030006 Peoples R China

In this article, in order to solve the problem of linear array synthesis with upper and lower bounds and aperture constraints, the improved bat algorithm (IBA) is proposed. The IBA embeds an adaptive local search strategy to enhance population diversity. Compared with other algorithms in antenna sidelobe suppression simulation, the bat algorithm has higher accuracy and effectiveness, and does not need to adjust too many parameters.

关键词： constrained optimization problem improved bat algorithm linear antenna arrays sidelobe level sparse arrays

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Cleaning decision model of MBR membrane based on Bandelet neural network optimized by improved bat algorithm

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APPLIED SOFT COMPUTING 2020年 91卷 106211-106211页

作者： Zhao, Bin Chen, Hao Gao, Diankui Xu, Lizhi Zhang, Yuanyuan Liaoning Shihua Univ Sch Mech Engn Fushun 113001 Liaoning Peoples R China

The membrane fouling is an important factor of restricting wide application of MBR (Membrane Bio-Reactor), which causes the fall of membrane flux and reduces the membrane cleaning period. So the Bandelet neural network is proposed through combining Bandelet transform and neural network, which predicts membrane flux and its recovery rate for making proper membrane cleaning decision. Firstly, the main affecting factors of membrane fouling are discussed. Secondly, the architecture of Bandelet neural network is designed with Bandelet function and its scale function as activation functions of hidden and output layers respectively. Thirdly, the improved bat algorithm is established, which is applied to improve the optimization effect of parameters of Bandelet neural network. Finally, the simulation analysis is carried out, the improved bat algorithm has higher performance than the traditional bat algorithm through analyzing the single objective optimization problem from 2018 CEC competition, the optimal number of nodes in hidden layer is confirmed based on comparison analysis and statistical tests. The proposed BNN-IBA has obvious superiority in prediction accuracy and speed according to prediction simulation results of membrane fouling of MBR, which has better prediction results than other state-of-art prediction models optimized by the novel optimal algorithms. In addition, the proper membrane cleaning period and method are confirmed according to the prediction results of membrane flux and its recovery rate. (C) 2020 Elsevier B.V. All rights reserved.

关键词： MBR Membrane cleaning Bandelet neural network improved bat algorithm Prediction

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Investigating the dimensions of globalization and its impact on poverty in Iran: An improved bat algorithm approach

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METHODSX 2021年 8卷 101210页

作者： Robati, Fatima Nazari Akbarifard, Hossein Jalaee, Seyyed Abdolmajid Shahid Bahonar Univ Kerman Fac Management & Econ Dept Econ Kerman Iran

Collective intelligence is one of the most powerful optimization techniques based on group behaviors of organisms. bat algorithm (BA) is an algorithm inspired by the collective action of bats in the wild, presented in 2010 by Yang. Researchers have made several efforts to improve these algorithms. This article investigates the effect of globalization on Iran's poverty by enhancing the performance of BA. As an inescapable reality, globalization has various political, social, and economic dimensions, each with different effects on poverty. In this article, to improve the algorithm's performance, the speed and motion relationships of bats were modified such that to adapt the movement of bats as optimization solutions toward the target. The mutation operator is also used to check all points of the search space to get rid of the optimal local optimization. The study period is the years 1995 to 2017. The results showed that globalization affects Iran's poverty in various dimensions, and the performance of the improved bat collective intelligence algorithm (ISABA) for modeling is better than that of the bat algorithms (BAs). This article provides a suitable method for researchers to study poverty. improved BAs can help researchers solve complex problems. The results obtained by the collective intelligence algorithms in this paper help researchers in the field of poverty to compare the results of their research with it. (C) 2021 Published by Elsevier B.V.

关键词： Globalization Poverty improved bat algorithm Optimization

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Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by improved bat algorithm Considering Energy Price Factors

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ENERGIES 2020年第13期13卷 3471-3471页

作者： Sun, Wei Zhang, Junjian North China Elect Power Univ Dept Econ & Management Baoding 071000 Peoples R China

In response to climate change and environmental issues, many countries have gradually optimized carbon market management and improved the carbon market trading mechanism. Carbon price prediction plays a pivotal role in promoting carbon market management when investors are guided by prediction to conduct rational carbon trading. A novel carbon price prediction methodology is constructed based on ensemble empirical mode decomposition, improved bat algorithm, and extreme learning machine (EEMD-IBA-ELM) in this study. Firstly, the carbon price is decomposed into multiple regular intrinsic mode function (IMF) components by the ensemble empirical mode decomposition, and partial autocorrelation analysis (PACF) is used to find IMF historical data affecting the current value of IMF. Secondly, the improved bat algorithm (IBA) is used to heighten extreme learning machine (ELM) while adaptive parameters are obtained. Finally, EEMD-IBA-ELM was established to predict carbon price. Simultaneously, energy price fluctuation is introduced into the carbon price prediction model. As a consequence, EEMD-IBA-ELM carbon price prediction ability is further improved. In the empirical analysis, the historical carbon price of European Climate Exchange (ECX) and Korea Exchange (KRX) markets are used to examine the effectiveness and stability of the model. Errors of carbon price prediction in ECX and KRX is 2.1982% and 1.1762%, respectively. The results show that the EEMD-IBA-ELM carbon price prediction model can accurately predict carbon price when prediction effect shows strong stability. Furthermore, carbon price prediction accurateness was significantly enhanced by using energy price fluctuation as an influencing factor of carbon price prediction.

关键词： ensemble empirical mode decomposition improved bat algorithm extreme learning machine energy price fluctuation

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