检索结果-内蒙古大学图书馆

A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction

引用

ENERGY 2023年第1期266卷

作者： Xiong, Jinlin Peng, Tian Tao, Zihan Zhang, Chu Song, Shihao Nazir, Muhammad Shahzad Huaiyin Inst Technol Fac Automation Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Accurate wind power forecast is critical to the efficient and safe running of power systems. A hybrid model that combines complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), random forest (RF), improved reptile search algorithm (IRSA), bidirectional long short-term memory (BiLSTM) network and extreme learning machine (ELM) is proposed for wind power prediction in this paper. Firstly, the CEEMD decomposes the non-stationary original wind power sequence into comparatively stationary modal components, and sample entropy aggregation is used to decrease the computational complexity. Secondly, redundant features are further eliminated through random forest feature selection. Thirdly, the BiLSTM model and the ELM model are applied to forecast high and low frequency components, respectively. IRSA is used to optimize the model's parameters. Finally, the predicted value of each component is summed to arrive at the final predicted value of wind power. By comparing with ten other models, the results show that the dual-scale ensemble model of BiLSTM and ELM can obtain better prediction accuracy. The RMSE of the model proposed in this study is reduced by more than 10% compared with other benchmark models, which demonstrates that the proposed model can better fit the wind power data and achieve better prediction results.

关键词： Wind power prediction BiLSTM ELM CEEMD reptile search algorithm

来源：评论

学校读者我要写书评

暂无评论

An Efficient Task Allocation with Fuzzy reptile search algorithm for Disaster Management in urban and rural area

引用

SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2023年 39卷

作者： Chaudhry, Rashmi Rishiwal, Vinay Netaji Subhash Univ Technol Delhi India MJP Rohilkhand Univ Bareilly Uttar Pradesh India

This paper proposes an architecture for rescue operations in fire disasters by modeling the behavior generated tasks scheduling to the fog or cloud devices. The problem is formulated with a dynamic optimization problem enriched with the support of task priority and task offloading functions. The goal of the algorithm is to schedule tasks by taking into account the location of each device and the available computing power each processing device. As Fog computing has been proven to be an excellent choice for applications requiring lower delay requirements, the fundamental challenge of constraint-based problems can be effectively solved utilizing the distributed environment of fog computing. The proposed algorithm, An Efficient Task Allocation using Fuzzy reptile search algorithm for Disaster Management (ETARSA-DM), addresses the issue of balanced load and total energy consumption in an integrated fog/cloud platform. It consists of two phases;in first one, priority of tasks is categorized with a fuzzy system, and in the second phase, an improved reptile search algorithm (RSA) is used with a novel validation function for real-time offloading of tasks in case of exceeding of a deadline at any device. This offloading considers the node's proximity, generating the tasks for lesser delay and more throughput. The performance of the proposed algorithm is evaluated, and obtained results demonstrate its out-performance compared to the state-of-the-art.

关键词： Disaster management Task allocation reptile search algorithm Fuzzy systems

来源：评论

学校读者我要写书评

暂无评论

Efficient Task Offloading Using Ant Colony Optimization and reptile search algorithms in Edge Computing for Things Context

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第9期15卷 588-596页

作者： Zhang, Ting Guo, Xiaojie Jiaozuo Univ Sch Informat Engn Jiaozuo 454000 Peoples R China

widespread use of Internet of Things (IoT) technology has triggered unparalleled data creation and processing needs, necessitating effective computation offloading solutions. Conventional edge computing approaches have difficulties in dealing with rising energy usage issues and task allocation delays. This study introduces a novel hybrid metaheuristic algorithm called ACO-RSA, which synergizes two metaheuristic algorithms, Ant Colony Optimization (ACO) and reptile search algorithm (RSA). The proposed approach addresses the energy and latency issues associated with offloading computations in IoT edge computing environments. A comprehensive system design that effectively encapsulates the uplink transmission communication model and a personalized multi-user computing task load model is developed. The system considers various constraints, such as network latency, task complexity, and available computing resources. Based on this, we formulate an optimization objective suitable for computing outsourcing in the IoT ecosystem. Simulations conducted in a real- world IoT scenario demonstrate that ACO-RSA significantly reduces both time delay and energy consumption compared to benchmark algorithms, achieving up to 27.6% energy savings and 25.4% reduction in time delay. ACO-RSA exhibits robustness and scalability when optimizing task offloading in IoT edge computing environments.

关键词： Task offloading edge computing ant colony optimization reptile search algorithm Internet of Things energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Unleashing Chaos: Enhanced reptile search for the Set Covering Problem 4th

Unleashing Chaos: Enhanced Reptile Search for the Set Coveri...

引用

4th International Conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-Annual

作者： Cisternas-Caneo, Felipe Barrera-Garcia, Jose Crawford, Broderick Soto, Ricardo Becerra-Rozas, Marcelo Giachetti, Giovanni Monfroy, Eric Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Andres Bello Santiago Chile Univ Angers LERIA Angers France

ISBN: (纸本)9783031832093;9783031832109

This study investigates the combination of binarization methods and chaotic maps within the reptile search algorithm to address binary combinatorial optimization challenges, specifically concentrating on the Set Covering Problem. Binarization in metaheuristics is critical for transforming continuous search spaces into discrete ones, which is essential for efficiently solving binary problems. We investigate the impact of chaotic maps, precisely the chaotic map type "sine", to enhance the stochastic components of metaheuristics, facilitating robust broadening and refinement of the search space. Our experimental analysis compares the performance of the reptile search algorithm, enhanced with different binarization strategies, in comparison to established metaheuristics like the well-known Particle Swarm Optimization and the popular Grey Wolf Optimizer. The results demonstrate that the reptile search algorithm with elitist binarization strategies, particularly when integrated with chaotic maps, significantly outperforms other algorithms to achieve near-optimal solutions with minimal variance. These findings highlight the effectiveness of sophisticated binarization strategies and the potential of chaotic maps to refine the search capabilities of metaheuristics in complex optimization scenarios.

关键词： Metaheuristics Combinatorial Problems reptile search algorithm Binarization Chaotic Maps

来源：评论

学校读者我要写书评

暂无评论

Hybrid reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

引用

Journal of Bionic Engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of reptile search algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram reptile search algorithm Snake Optimizer Optimization

来源：评论

学校读者我要写书评

暂无评论

Ensemble learning prediction model for lithium-ion battery remaining useful life based on embedded feature selection

引用

APPLIED SOFT COMPUTING 2025年 169卷

作者： Wang, Xiao-Tian Zhang, Song-Bo Wang, Jie-Sheng Liu, Xun Sun, Yun-Cheng Shang-Guan, Yi-Peng Zhang, Ze-Zheng Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

To prevent potential dangers, it is crucial to accurately predict the Remaining Useful Life (RUL) of Lithium-Ion Batteries (LIBs) as capacity gradually decreases during use. Based on the Stanford MIT Battery Life Data, our researchers find the selection of input features difficult. Therefore, a new embedded Feature Selection (FS) method (LS-Embed-BHRSA) was proposed, which combines the Laplace score (LS) algorithm with the Binary Hybrid reptile search algorithm (BHRSA). It is an organic fusion method of filtering and wrapping, composing an embedding structure that can quickly and accurately find the best feature subset. In addition, a Stacking multimodel integration strategy was also proposed to completely use the advantages of different models and improve the prediction accuracy of LIBs' RUL. The validation results showed that the Hybrid reptile search algorithm (HRSA) performed the best on the 2022 test function, and LS-Embed-BHRSA found the best features in comparison with other FS architectures on the UCI classification data. The testing results of the MIT data show that the overall proposed prediction model exceeds other machine learning models in metrics, such as RMSE, MAE, MAPE and R2, indicating its strong competitiveness.

关键词： Lithium-ion battery remaining useful life Feature selection Stacking multi-model integration reptile search algorithm Adaptive convergence factor

来源：评论

学校读者我要写书评

暂无评论

A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM

引用

BUILDINGS 2025年第3期15卷 357-357页

作者： Liu, Pei Gu, Hao Gu, Chongshi Wang, Yanbo Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210098 Peoples R China

This paper presents a deformation prediction model for concrete dams that integrates a reptile search algorithm (RSA), a Variational Mode Decomposition (VMD) algorithm, and a long short-term memory network model with attention mechanism (AttLSTM). This model utilizes the RSA to optimize the parameters K and alpha of the VMD algorithm. It combines the variance of the modified mode with the sample entropy of these data as the objective function, effectively converting monitoring data into a stable signal while retaining essential characteristic variation. Data are reformatted into a three-dimensional structure and partitioned into training and testing sets. The AttLSTM network was applied to forecast deformation, and results were validated using practical engineering cases. The performance of the proposed model was compared against that of four other models: LSTM, VMD-LSTM, attention LSTM, and VMD-AttLSTM models. Analysis of the five evaluation criteria revealed that the RSA can better optimize the parameters of the VMD algorithm. Consequently, the proposed model demonstrates superior noise reduction capabilities and improved prediction accuracy.

关键词： deformation prediction model reptile search algorithm variable mode decomposition long short-term memory network model with attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Internet of Things Enabled DDoS Attack Detection Using Pigeon Inspired Optimization algorithm with Deep Learning Approach

引用

Computers, Materials & Continua 2024年第9期80卷 4047-4064页

作者： Turki Ali Alghamdi Saud S.Alotaibi Department of Computer Science and Artificial Intelligence College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,*** Denial of Service(DoS)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT *** learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a DoS *** uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and ***,this study presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT *** PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-Service(DDoS)attack detection in an IoT ***,the PIODL-ADC model utilizes Z-score normalization to scale input data into a *** handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s ***,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDoS ***,reptile search algorithm(RSA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC *** experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.

关键词： Internet of things denial of service deep learning reptile search algorithm feature selection

来源：评论

学校读者我要写书评

暂无评论

Optimizing signal smoothing using HERS algorithm and time fractional diffusion equation

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartF期238卷

作者： Jayaraj, Amutha Praba Gounder, Kuppuswamy Nallappa Rajagopal, Jeetendra Nandha Engn Coll Dept Math Erode 638052 Tamil Nadu India Kalaignar Karunanidhi Inst Technol Dept Mech Engn Coimbatore Tamil Nadu India Kongu Engn Coll Dept Math Perundurai Tamil Nadu India

Signal processing is often affected by various sources of noise that can distort or modify the signals. Removing these noises from the original signal is a crucial step in signal processing, and researchers have proposed several approaches to address this issue. However, achieving an optimized solution remains a challenge. In this study, we introduce a novel approach called the Hybrid Ebola-based reptile search (HERS) model based on Time Fractional Diffusion Equation (TFDE). The TFDE is a conventional diffusion equation used for preserving the peak smoothness of spectra signals. In our proposed technique, we consider the processing spectrum of the signal as the reference signal, which serves as the design for the diffusion equation. By applying the diffusion function, we achieve signal peak preservation and smoothing, referred to as the filtering of diffusion. One potential challenge with the time fractional order diffusion equation is its susceptibility to variations in the time step size. To address this, we employ the HERS algorithm to select an optimal time step size that enables efficient signal smoothing. To validate the effectiveness of the proposed technique, we conduct simulations and compare the results with conventional techniques such as the wavelet model, Savitzky-Golay, and regularization techniques. The per-formance evaluation confirms the superiority of our proposed HERS-TFDE approach in noise removal and signal smoothing. This research aims to contribute to the development of an optimized solution for noise removal in signal processing, leveraging the Hybrid Ebola reptile search algorithm and TFDE. The findings have the po-tential to enhance various signal-processing applications where noise reduction is critical.

关键词： Diffusion equation Spectrum signal Peal preserving smoothing signal Ebola search algorithm Diffusion filtering reptile search algorithm

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

引用

APPLIED SOFT COMPUTING 2024年 159卷

作者： Golafshani, Emadaldin Mohammadi Behnood, Ali Kim, Taehwan Ngo, Tuan Kashani, Alireza Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia Univ Mississippi Dept Civil Engn University MS 38677 USA Univ Melbourne Infrastruct Engn Dept Melbourne Vic 3010 Australia

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion in recycled aggregate concrete (RAC) compared to natural aggregate concrete. Traditional carbonation depth assessment methods in RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There is a noted deficiency in the application of machine learning techniques for accurately predicting carbonation depth in RAC, a gap this study aims to fill. Utilizing the extreme gradient boosting (XGBoost) technique, recognized for its efficacy in ensemble machine learning, this study innovates in modeling carbonation depth in RAC. It emphasizes the criticality of hyperparameter optimization of the XGBoost algorithm for maximizing model accuracy. To achieve this, three novel metaheuristic optimization algorithms, including reptile search algorithm (RSA), Aquila optimizer (AO), and arithmetic optimization algorithm (AOA), were introduced as global optimizers for tunning the XGBoost hyperparameters. The study was underpinned by a comprehensive database compiled from extensive literature, facilitating the development of an accurate RAC carbonation depth model. Through rigorous evaluations, including sensitivity analyses, the Wilcoxon signed-rank test, and runtime comparisons, the synthesized models demonstrated exceptional accuracy, with coefficients of determination exceeding 0.95. The XGBoost-AO algorithm, in particular, showcased superior performance, with the XGBoost-RSA algorithm providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions as significant carbonation depth influencers. A userfriendly graphical user interface was developed, enhancing the practical utility of the findings for predicting carbonation depth progression in RAC over time. This research significantly advances the predictive accuracy for carbonation depth in RAC, contributing to the

关键词： Recycled aggregate concrete Carbonation depth XGboost ensemble technique Equilibrium optimizer Arithmetic optimization algorithm reptile search algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：