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

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

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Automated sign language detection and classification using reptile search algorithm with hybrid deep learning

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HELIYON 2024年第1期10卷 e23252页

作者： Alsolai, Hadeel Alsolai, Leen Al-Wesabi, Fahd N. Othman, Mahmoud Rizwanullah, Mohammed Abdelmageed, Amgad Atta Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Riyadh Saudi Arabia Future Univ Egypt New Cairo Fac Comp & Informat Technol Dept Comp Sci New Cairo 11835 Egypt Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Alkharj Saudi Arabia

Sign language recognition (SLR) contains the capability to convert sign language gestures into spoken or written language. This technology is helpful for deaf persons or hard of hearing by providing them with a way to interact with people who do not know sign language. It is also be utilized for automatic captioning in live events and videos. There are distinct methods of SLR comprising deep learning (DL), computer vision (CV), and machine learning (ML). One general approach utilises cameras for capturing the signer's hand and body movements and processing the video data for recognizing the gestures. One of challenges with SLR comprises the variability in sign language through various cultures and individuals, the difficulty of certain signs, and require for realtime processing. This study introduces an Automated Sign Language Detection and Classification using reptile search algorithm with Hybrid Deep Learning (SLDC-RSAHDL). The presented SLDC-RSAHDL technique detects and classifies different types of signs using DL and metaheuristic optimizers. In the SLDC-RSAHDL technique, MobileNet feature extractor is utilized to produce feature vectors, and its hyperparameters can be adjusted by manta ray foraging optimization (MRFO) technique. For sign language classification, the SLDC-RSAHDL technique applies HDL model, which incorporates the design of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). At last, the RSA was exploited for the optimal hyperparameter selection of the HDL model, which resulted in an improved detection rate. The experimental result analysis of the SLDC-RSAHDL technique on sign language dataset demonstrates the improved performance of the SLDC-RSAHDL system over other existing DL techniques.

关键词： Sign language Deep learning Computer vision reptile search algorithm Intelligent models

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

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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

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

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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

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

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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

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Predicting mode I fracture toughness of CCNBD quasi-brittle specimens using hybrid gradient boosting algorithms

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THEORETICAL AND APPLIED FRACTURE MECHANICS 2025年 138卷

作者： Rouhani, Mohammad Matin Dolatshahi, Alireza Amirkabir Univ Technol Dept Min Engn Tehran Iran

This study examines the prediction of Mode I fracture toughness in Cracked Chevron Notched Brazilian Disc (CCNBD) specimens by integrating hybrid machine learning (ML) algorithms-specifically the XGBoost, the CatBoost, and the LightGBM-with metaheuristic optimization techniques This study specifically employs the reptile search algorithm (RSO) and the Dingo Optimization algorithm (DOA) for hyperparameter tuning to further optimize model performance. Using a dataset of 150 samples of rock and cement-based materials, the results show that the RSO-CatBoost model has an impressive performance both in training (R2 = 0.9825) and in testing (R2 = 0.98), while the DOA-CatBoost model also shows a good performance. Besides the ML models, novel empirical models for estimating fracture toughness are presented in this study, which overcomes the historical limitation of classical formulas using the addition of geometric and mechanical parameters. With an R2 of 0.664 versus a maximum of 0.582 for previous equations, the empirical models outperform the existing equations highlighting their increased predictive power. Combining these methods offers a considerable increase in prediction accuracy as well as valuable interpretation concerning the role of tensile strength and specimen geometry on fracture toughness. This work, therefore, offers a dual contribution: advanced predictive models via ML with refined hyperparameter tuning and improved empirical equations that better capture the complex behavior of quasi-brittle materials, reducing the need for extensive physical testing.

关键词： Quasi-brittle Materials Fracture Toughness XGBoost LightGBM CatBoost reptile search algorithm Dingo Optimization algorithm

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Novel distance-fitness learning scheme for ameliorating metaheuristic optimization

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 65卷

作者： Celik, Emre Houssein, Essam H. Abdel-Salam, Mahmoud Oliva, Diego Tejani, Ghanshyam G. Ozturk, Nihat Sharma, Sunil Kumar Baljon, Mohammed Duzce Univ Engn Fac Dept Elect & Elect Engn Duzce Turkiye Minia Univ Fac Comp & Informat Dept Comp Sci Al Minya Egypt Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Univ Guadalajara Dept Ingn Electrofoton Guadalajara Jalisco Mexico Saveetha Univ Saveetha Dent Coll & Hosp Saveetha Inst Med & Tech Sci Dept Res Analyt Chennai 600077 India Yuan Ze Univ Dept Ind Engn & Management Taoyuan 320315 Taiwan Gazi Univ Technol Fac Dept Elect & Elect Engn Ankara Turkiye Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Majmaah 11952 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Comp Engn Majmaah 11952 Saudi Arabia Minia Natl Univ Al Minya Egypt

An important portion of metaheuristic algorithms is guided by the fittest solution obtained so far. searching around the fittest solution is beneficial for speeding up convergence, but it is detrimental considering local minima stagnation and premature convergence. A novel distance-fitness learning (DFL) scheme that provides better searchability and greater diversity is proposed to resolve these. The method allows search agents in the population to actively learn from the fittest solution, the worst solution, and an optimum distance-fitness (ODF) candidate. This way, it aims at approaching both the fittest solution and ODF candidate while at the same time moving away from the worst solution. The effectiveness of our proposal is evaluated by integrating it with the reptile search algorithm (RSA), which is an interesting algorithm that is simple to code but suffers from stagnating in local minima, converging too early, and a lack of sufficient global searchability. Empirical results from solving 23 standard benchmark functions, 10 Congresses on Evolutionary Computation (CEC) 2020 test functions, and 2 real-world engineering problems reveal that DFL boosts the capability of RSA significantly. Further, the comparison of DFL-RSA with popular algorithms vividly signifies the potential and superiority of the method over most of the problems in terms of solution precision.

关键词： Metaheuristic reptile search algorithm Distance-fitness learning Global optimization

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Estimating the pile-bearing capacity utilizing a reliable machine-learning approach

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第4期8卷 1-32页

作者： Hu, Juan Xia, Cunjing Wu, Jiahong Dong, Fenghui Zhejiang Shuren Univ Sch Urban Construct Hangzhou 310015 Zhejiang Peoples R China Shandong Univ Jinan 250100 Shandong Peoples R China Design & Res Inst Grp Co Ltd Guangdong Transportat Planning Guangzhou 510507 Peoples R China Nanjing Forestry Univ Coll Civil Engn Nanjing 210037 Peoples R China

Accurately estimating Pile Bearing Capacity (PBC) is crucial for safe, economical pile foundation construction, ensuring optimal material use and enhanced structural integrity. The study focuses on developing a framework using the Multi-Layer Perceptron (MLP) model to predict PBC. MLP is selected for its effectiveness in modeling complex, non-linear data links. To enhance the MLP's performance, two optimization algorithms, African Vulture Optimization (AVO) and reptile search algorithm (RSA), are integrated. These algorithms are selected for their ability to balance exploration and exploitation, helping the MLP avoid local minima and achieve global optima in complex optimization tasks. The proposed AI models achieved remarkable accuracy, with the MLPS2 (MLP + RSA in the second layer of MLP) and MLAV2 (MLP + AVO in the second layer of MLP) models attaining substantial Coefficient of Determination (R2) values of 0.995 and 0.988, alternatively. The MLPS2 model also obtained Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values of 126.70 KN and 102.80 KN, respectively. The MLAV2 model was demonstrated as the second-best model among the proposed frameworks, with an RMSE value of 203.04 KN. The developed AI predictive frameworks based on MLP and hybrid optimization techniques effectively enhance PBC estimations, paving the way for improved pile design and optimization.

关键词： Pile Pile bearing capacity Multilayer perceptron African Vulture Optimization reptile search algorithm

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Multistage control for frequency and voltage stability in a multi-area power network with nonlinear dynamics

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RESULTS IN ENGINEERING 2025年 26卷

作者： Irfan, Rasmia Gulzar, Muhammad Majid Habib, Salman Shakoor, Adnan Ahmad, Hasnain Murtaza, Ali Faisal Univ Punjab Inst Elect Elect & Comp Engn Lahore Pakistan King Fahd Univ Petr & Minerals Dept Control & Instrumentat Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Biosyst & Machines Dhahran 31261 Saudi Arabia Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Saudi Arabia

Modern power systems are composed of both renewable and conventional sources, due to mismatch in generation and demand and non-linearities of power system, face significant challenges of load frequency control and voltage regulation, so they require an intelligent control strategy for stable operation. In this regard, the reptile search algorithm (RSA) tuned optimal cascaded two-degree-of-freedom proportional integral control with a proportional derivative filter controller RSA-(CC-2DOF (PI-PDF)) is presented in this study. To ensure stable operation, this controller is employed for load frequency control and automatic voltage regulation. To enhance the stability, an interline power flow controller is integrated into the tie-line between the two areas. For a realistic approach, physical constraints such as generation rate constraints (GRCs) and communication time delays (CTDs) are incorporated into the system and analyzed with and without these constraints. To showcase the efficacy of the controller, its performance is compared with the state-of-the-art optimal controllers and to check the robustness proposed controller is subjected to random load variations. The proposed controller demonstrates superior proficiency as it achieves stabilization of frequency in minimum settling time of 3.21 sec and 13 sec in area 1 & area 2 respectively. This excellent response of controller outpaces the other controllers like IGWO-TID, CMO-FOPID, MFO-PIPD and CBO-PI-(1+PD), which shows much longer settling time i.e. 3.8 sec, - 6.45 sec, 6.2 sec and 3.7 sec for frequency to settle. Likewise, either zero or minimal undershoot and overshoot i.e., 0.00001 approximate to 0 Hz, -5.39 x 10- 3Hz for area 1 and 1 x 10-3Hz, -0.35 x 10- 3Hz for area 2 of the system is observed for the suggested controller in contrast to other controllers, where the highest overshoot of 0.018 Hz is observed for the CMO-FOPID controller. Similarly, for tie line power deviation stable response can be seen within

关键词： Electric power systems reptile search algorithm Load frequency control Automatic voltage regulation Renewable energy source Energy storage systems

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

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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

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