Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separatorMendeley Data by Marko Tesanovic, Torben Bardel, Robin Karl, Sonja Berensmeier

“…Furthermore, process efficiency is often not fully realized due to the reliance on fixed operational recipes.This study presents a digital twin framework for a pilot-scale HGMS system, integrating real-time monitoring, automated control, advanced mechanistic models, and multi-objective optimization using Bayesian algorithms. …”
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Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model by Yinping Cao, Fengying Fang, Guowei Wang, Wenyu Zhu, Yijie Hu

“…To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. …”
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Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload by Iver Bergh Hvidsten, Kristian Hovde Liland, Oliver Tomic, Jorge Mario Marchetti

“…This study explores non-linear tree-based learning algorithms for modeling biodiesel reactions. A dataset of 3038 reaction samples from 111 published studies was compiled, each optimizing distinct biodiesel reaction systems. …”
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Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach by Rasool Jader, Sadegh Aminifar

“…The suggested model uses the clustering KMeans technique for data reduction and the elbow method to find the optimal k value and the Mahalanobis distance method to find more related cluster to new samples, and the classification methods such as decision tree, random forest, SVM, KNN, logistic regression, and Naïve Bayes are used for prediction. …”
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An Experimental Study of Strategies to Control Diversity in Grouping Mutation Operators: An Improvement to the Adaptive Mutation Operator for the GGA-CGT for the Bin Packing Proble... by Stephanie Amador-Larrea, Marcela Quiroz-Castellanos, Octavio Ramos-Figueroa

“…Grouping Genetic Algorithms (GGAs) are among the most outstanding methods for solving NP-hard combinatorial optimization problems by efficiently grouping sets of items. …”
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Research on adverse event classification algorithm of da Vinci surgical robot based on Bert-BiLSTM model by Tianchun Li, Wanting Zhu, Wenke Xia, Li Wang, Weiqi Li, Peiming Zhang

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A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling by Mengyun Yao, Minghua Hu, Jianan Yin, Jiaming Su, Mengxuan Yin

“…The NSGA-II algorithm, combining local search strategies (LS-NSGA-II), is used to solve the model. …”
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An Efficient Parametric Modeling, Evaluation and Optimization Strategy for Aerodynamic Configuration Design of eVTOL Aircraft by Yiming Du, Zehao Liu, Meizhu Shen, Jiang Wu, Kexin Zhang, Chuanye Jiang, Jiale Zhong

“…A phased collaborative aerodynamic design strategy for eVTOL aircraft was established, by combining the OpenVSP platform for rapid parametric modeling and evaluation, a Kriging surrogate framework with an improved differential evolution algorithm for optimization, and the SUAVE platform for propeller reverse design. …”
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Photovoltaic power prediction system based on dual-layer decomposition strategy and a novel dynamic grouping multi-objective coati optimization algorithm by Xiaole Tang, Hao Lu, Yanting Kang, Wenjun Zhao

“…The substantial volatility of photovoltaic (PV) power output presents challenges to the stable operation of power grids. To improve the accuracy and stability of PV power prediction, this study proposes a PV power prediction system based on a dual-layer decomposition strategy and a dynamic grouping multi-objective Coati optimization algorithm (DGMOCOA). …”
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Improving distributed systems failure prediction via multi-objective feature selection and deep forest by Zhidan Yuan, Yikai Zhang, Yingqi Yu, Taizhi Lv, Tao Huang

“…This optimization process is achieved through the NSGA-III algorithm. …”
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Comprehensive Evaluation Method of Teaching Effect Based on Particle Swarm Optimization Neural Network Model by Heng Cao, Qianhui Gao

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A Mixed Integer Linear Formulation and a Grouping League Championship Algorithm for a Multiperiod-Multitrip Order Picking System with Product Replenishment to Minimize Total Tardin... by Morteza Farhadi Sartangi, Ali Husseinzadeh Kashan, Hassan Haleh, Abolfazl Kazemi

“…For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. …”
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Advancing Dynamic Emergency Route Optimization with a Composite Network Deep Reinforcement Learning Model by Jin Zhang, Hao Xu, Ding Liu, Qi Yu

“…This method utilizes the actor–critic framework, combined with attention mechanisms, pointer networks, and long short-term memory neural networks, to determine effective disaster relief path, and it compares the obtained scheduling scheme with the results obtained from the DRL algorithm based on the single-network model and ant colony optimization (ACO) algorithm. …”
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Enhancing E-Nose Performance via Metal-Oxide Based MEMS Sensor Arrays Optimization and Feature Alignment for Drug Classification by Ruiwen Kong, Wenfeng Shen, Yang Gao, Dawu Lv, Ling Ai, Weijie Song, Ruiqin Tan

“…This article introduces a novel approach to improve electronic nose classification accuracy by optimizing sensor arrays and aligning features. …”
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Enhancing Neural Network Training Through Neuroevolutionary Models: A Hybrid Approach to Classification Optimization by Hyasseliny A. Hurtado-Mora, Luis A. Herrera-Barajas, Luis J. González-del-Ángel, Roberto Pichardo-Ramírez, Alejandro H. García-Ruiz, Katea E. Lira-García

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Recovering 3D Basin Basement Relief Using High-Precision Magnetic Data Through Particle Swarm Optimization and Back Propagation Algorithm by Shen Yan, Xinjun Zhang, Zhongda Shang, Kai Wang, Yixin Ma

“…Feature attributes were extracted, and the Gini importance was used to quantify feature factor contributions, screen out effective features, and improve algorithm efficiency. Validity and practicality were verified through an analysis of the theoretical and noise models. …”
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Methods and Algorithms for Decision-Making in Agro-Industrial Environmental Management by Olga Ivashchuk, Vyacheslav Fedorov, Zhanat Kenzhebayeva, Oleg Ivashchuk, Moldir Allaniyazova, Kaiyrbek Makulov, Gulnar Kaziyeva

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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. …”
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A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression by Tingkai Hou, Zonghong Zhou, Yonggang Zhang, Jing Zhang

“…The conventional techniques used to assess the RMMPs face considerable challenges in real-world applications, which necessitates the need to investigate novel approaches.MethodsThis paper proposes a displacement back-analysis (DBA) approach that utilizes support vector regression (SVR) optimized by differential evolution grey wolf algorithm (DE-GWO) to invert the RMMPs, which improves global optimization capability and inversion accuracy. …”
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An integrated optimization model of network behavior victimization identification based on association rule feature extraction by Shengli ZHOU, Linqi RUAN, Rui XU, Xikang ZHANG, Quanzhe ZHAO, Yuanbo LIAN

“…The identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior victimization identification based on association rule feature extraction was proposed.The interactive traffic data packets generated when users accessed websites were captured by the model, and the implicit and explicit behavior features in network traffic were extracted.Then, the association rules between features were mined, and the feature sequences were reconstructed using the FP-Growth algorithm.Finally, an analysis model of telecom network fraud victimization based on network traffic analysis was established, combined with the stochastic forest algorithm of particle swarm optimization.The experiments show that compared with general binary classification models, the proposed model has better precision and recall rates and can effectively improve the accuracy of network fraud victimization identification.…”
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