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Prediction of Liquid Accumulation Height in Gas Well Tubing Using Integration of crayfish optimization algorithm and XGBoost

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PROCESSES 2024年第9期12卷 1788页

作者： Xia, Wenlong Liu, Botao Xiang, Hua Yangtze Univ Hubei Key Lab Oil & Gas Drilling & Prod Engn Jingzhou Peoples R China

The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the crayfish optimization algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA-XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA-XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells.

关键词： gas well fluid accumulation prediction model machine learning XGBoost crayfish optimization algorithm

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Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Yan, Dongmei Wang, Hongkun Gao, Yujie Tian, Shiji Zhang, Hong Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China Xinjiang Prod & Construct Corps Key Lab Modern Ag Shihezi 832003 Peoples R China Minist Agr & Rural Affairs Key Lab Northwest Agr Equipment Shihezi 832003 Peoples R China

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum crayfish optimization algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.

关键词： Multi-microgrid system Optimal scheduling crayfish optimization algorithm Shared energy storage Stackelberg game

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Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Ragab, Mahmoud Asar, Turky Omar King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia Univ Jeddah Coll Sci & Arts Alkamil Dept Biol Jeddah Saudi Arabia

Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a gre

关键词： Oral cancer Bilateral filtering crayfish optimization algorithm Histopathological image Bidirectional long short-term memory Squeeze-excitation- CapsNet

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Adaptive crayfish optimization algorithm for multi-objective scheduling optimization in distributed production workshops

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Yang, Xin Yang, Xiaoying Du, Jinhao Henan Univ Sci & Technol Sch Business Luoyang 471023 Peoples R China Henan Inst Technol Sch Management Xinxiang 453000 Peoples R China Henan Univ Sci & Technol Sch Mechatron Engn Luoyang 471003 Peoples R China Wenzhou Univ Sch Mech & Elect Engn Wenzhou 325035 Peoples R China

The increasing demand for wind turbines and cost pressures in the wind energy industry have made the Wind Turbine Pultruded Panels Production Scheduling Problem (WTPP-PSP) a critical challenge. To address the production scheduling requirements of WTPP-PSP, an intelligent platform is proposed for wind turbine pultruded panel production systems, leveraging intelligent decision-making to tackle the problem. A multi-objective model based on mixed-integer linear programming is developed, considering sequence-dependent completion and setup time constraints. The model aims to maximize customer satisfaction, minimize total setup time, and reduce deviations in workshop machine loads. To solve this problem, an Adaptive crayfish optimization algorithm (ACOA) is introduced. This algorithm incorporates crossover and mutation operators, making it effective for discrete optimization problems. Furthermore, an improved crowding distance calculation enhances the algorithm's performance in multi-objective optimization by improving solution distribution. Reinforcement learning is employed to dynamically adjust temperature parameters, improving both exploration and exploitation capabilities and thus enhancing the convergence of the algorithm. The performance comparison using multi-objective metrics such as HV, IGD, GD, and NR demonstrates that ACOA significantly outperforms COA, WOA, and NSGA-II, with average improvements of 76%, 80%, 28%, and 220%, respectively. These results highlight ACOA's consistent advantages in coverage, convergence, and solution diversity. In the application to WTPP-PSP, the proposed algorithm outperforms COA by approximately 13%, 10%, and 8% in the three objectives.

关键词： Multi-objective optimization crayfish optimization algorithm Reinforcement learning Production scheduling Distributed production workshops

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MCOA: A Multistrategy Collaborative Enhanced crayfish optimization algorithm for Engineering Design and UAV Path Planning

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期2025卷

作者： Xiao, Yaning Cui, Hao Khurma, Ruba Abu Hussien, Abdelazim G. Castillo, Pedro A. Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China Al Balqa Appl Univ Al Huson Univ Coll Informat Technol Dept Irbid 19117 Jordan Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden Fayoum Univ Fac Sci Faiyum 63514 Egypt Univ Granada Dept Comp Engn Automat & Robot Granada 18071 Spain

The crayfish optimization algorithm (COA) is a recent bionic optimization technique that mimics the summer sheltering, foraging, and competitive behaviors of crayfish. Although COA has outperformed some classical metaheuristic (MH) algorithms in preliminary studies, it still manifests the shortcomings of falling into local optimal stagnation, slow convergence speed, and exploration-exploitation imbalance in addressing intractable optimization problems. To alleviate these limitations, this study introduces a novel modified crayfish optimization algorithm with multiple search strategies, abbreviated as MCOA. First, specular reflection learning is implemented in the initial iterations to enrich population diversity and broaden the search scope. Then, the location update equation in the exploration procedure of COA is supplanted by the expanded exploration strategy adopted from Aquila optimizer (AO), endowing the proposed algorithm with a more efficient exploration power. Subsequently, the motion characteristics inherent to L & eacute;vy flight are embedded into local exploitation to aid the search agent in converging more efficiently toward the global optimum. Finally, a vertical crossover operator is meticulously designed to prevent trapping in local optima and to balance exploration and exploitation more robustly. The proposed MCOA is compared against twelve advanced optimization algorithms and nine similar improved variants on the IEEE CEC2005, CEC2019, and CEC2022 test sets. The experimental results demonstrate the reliable optimization capability of MCOA, which separately achieves the minimum Friedman average ranking values of 1.1304, 1.7000, and 1.3333 on the three test benchmarks. In most test cases, MCOA can outperform other comparison methods regarding solution accuracy, convergence speed, and stability. The practicality of MCOA has been further corroborated through its application to seven engineering design issues and unmanned aerial vehicle (UAV) path plann

关键词： crayfish optimization algorithm engineering optimization expanded exploration strategy L & eacute vy flight specular reflection learning vertical crossover operator

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Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Zhang, Yihang Zhao, Hang Zhejiang Prov Peoples Hosp Informat Ctr Hangzhou 310014 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Sch Phys & Optoelect Engn Hangzhou 310024 Peoples R China

The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart based on the FitzHugh-Nagumo (FHN) model was established for cardiac electrophysiology simulation and the ECG signals were acquired from the FEM results of representative points. Two different kinds of arrhythmia characterized by major anomalies of parameters a and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} in the FHN model were simulated, and the synthetic ECG signals were obtained respectively. A multi-objective optimization method based on non-dominated sorting was incorporated into the crayfish optimization algorithm to optimize the key parameters in VMD, then a variational mode decomposition technique for ECG signal processing based on a multi-objective crayfish optimization algorithm (MOCOA-VMD) was proposed, wherein the spectral kurtosis and KL divergence were determined as the indicators for decomposition. The Pareto optimal front was generated by MOCOA and the intrinsic mode functions of VMD with the best combination of K and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} were obtained. A deep attention model based on MOCOA-VMD was constructed for ECG signal classification. The ablation study was implemented to verify the effectiveness of the proposed signal decomposition method and deep attentio

关键词： Arrthythmia signal classification Attention scheme Finite element method crayfish optimization algorithm Variational mode decomposition Bayesian optimization

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crayfish optimization-based secure encryption of medical images with 7D hyperchaotic maps

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-21页

作者： David, Pauline Freeda Kothandapani, Suganya Devi Pugalendhi, Ganesh Kumar IFET Coll Engn Dept Comp Sci & Engn Villupuram 605108 Tamilnadu India Natl Inst Technol Silchar Dept Comp Sci & Engn Silchar 788010 Assam India Anna Univ Coll Engn Dept Comp Sci & Engn Chennai 600025 Tamilnadu India

Medical images (MI) contain both diagnostic information and sensitive personal data and they are usually exchanged between doctors, patients, hospitals and public networks. Therefore, it is essential to ensure safety during storage and transportation to protect the privacy of a patient. However, conventional cryptographic techniques are not effective for handling the unique characteristics of digital images like substantial pixel redundancy, high correlation, and sizable dimensions to provide proper security. Hence, the development of specialized image encryption algorithms becomes apparent as existing techniques are not reliable solutions. This issue prompted the enhancement of numerous low computational complexity approaches for encrypting MI. This paper introduces a crayfish optimization algorithm-based hybrid Seven-dimensional hyperchaotic image encryption technique for MI encryption and decryption. The image encryption integrates the 7D hyperchaotic maps, cellular automata, and bidirectional sequence diffusion techniques. The proposed encryption process involves data collection of DICOM images from MRI, X-ray, and CT scan datasets. In the encryption phase, chaotic scenes are generated using parameters optimized by the crayfish optimization algorithm, followed by confusion and diffusion phases. The decryption phase reverses these operations to restore the original image. Experimental results demonstrate this technique is effective in decryption and encryption performance as well as analysis of security such as correlation, information entropy, histogram, key space, differential attack, clipping and noise attacks, local information entropy speed analysis, and key sensitivity. The proposed method is safe and effective in protecting MI data.

关键词： Medical images Encryption Decryption 7D hyperchaotic graphs crayfish optimization algorithm Cryptography

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Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics

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ALEXANDRIA ENGINEERING JOURNAL 2025年 110卷 153-167页

作者： Alabdan, Rana Alabduallah, Bayan Alruwais, Nuha Arasi, Munya A. Asklany, Somia A. Alghushairy, Omar Alallah, Fouad Shoie Alshareef, Abdulrhman Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Al Majmaah 11952 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn POB 22459 Riyadh 11495 Saudi Arabia King Khalid Univ Coll Sci & Arts RijalAlmaa Dept Comp Sci Abha Saudi Arabia Northern Border Univ Fac Sci & Arts Dept Comp Sci & Informat Technol Ar Ar 91431 Saudi Arabia Univ Jeddah Coll Comp Sci & Engn Dept Informat Syst & Technol Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia

The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field;security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved crayfish optimization algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparametertuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval t

关键词： Consumer electronics Blockchain Internet of things Deep learning Intrusion detection system crayfish optimization algorithm

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Optimal allocation of capacitor banks and distributed generations to maximize conservation voltage reduction under diverse scenarios

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ENGINEERING optimization 2025年

作者： Lakra, Neha Smitha Bag, Baidyanath Natl Inst Technol Raipur Dept Elect Engn Raipur India

Conservation voltage reduction (CVR) is used by utilities to reduce energy consumption and losses. However, accurate positioning and sizing of capacitor banks (CBs) and distributed generation units (DGs) are necessary to enhance CVR capabilities. This article presents a two-stage approach to improve CVR capabilities. The first stage uses a crayfish optimization algorithm (COA) to find the best siting and sizing of CBs and DGs. The second stage applies an analytical method to determine on-load tap changer tap settings. The plausibility of the CVR approach is validated for voltage reduction, energy savings and emissions reduction on the IEEE-69 node system across different modes. Different load scenarios are evaluated for their impact on the CVR scheme. Simulation results demonstrate notable voltage reduction of up to 3.27%, energy savings of up to 11.50% and emissions reduction of 12.63%. The COA method surpasses existing algorithms and produces better results with a higher convergence rate.

关键词： Conservation voltage reduction capacitor banks distributed generation crayfish optimization algorithm energy savings

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Research on braking intention identification of electric vehicles based on COA-ELM algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年

作者： Li, Meiying Li, Chengxin Li, Xuebo Yu, Qiang Changan Univ Sch Automobile Xian 710064 Shaanxi Peoples R China

The identification of braking intention is crucial for enhancing driver assistance features, enhancing braking safety, and maximizing energy recovery efficiency of electric vehicles. To accurately identify braking intention, a novel identification model utilizing an extreme learning machine (ELM) and optimized by the crayfish optimization algorithm (COA) is proposed. Based on extensive braking test data, data processing, model training, and verification are conducted. The initial braking data are denoised using variational mode decomposition (VMD) and Shannon entropy, and the Gaussian mixture model (GMM) is employed to label the braking intention. The brake pedal opening, its change rate, and vehicle speed serve as inputs for the ELM model, with the braking intention label as the output. The COA is utilized to optimize the hidden layer parameters of ELM, thereby enhancing the precision of the intention identification model. The results indicate that, compared with the LSTM model, GRU model and ELM model, the accuracy of the COA-ELM model improves by 2.73%, 1.56%, and 0.39% respectively, with identification accuracy reaching over 99.02%. This offers a reliable modeling basis for the development of subsequent braking strategies.

关键词： crayfish optimization algorithm extreme learning machine braking intention identification

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