SGDO-SLAM: A Semantic RGB-D SLAM System with Coarse-to-Fine Dynamic Rejection and Static Weighted Optimization by Qiming Hu, Shuwen Wang, Nanxing Chen, Wei Li, Jiayu Yuan, Enhui Zheng, Guirong Wang, Weimin Chen

“…The experimental results demonstrate that SGDO-SLAM reduces the absolute trajectory error performance metrics by 95% compared to the ORB-SLAM2 algorithm, while maintaining real-time efficiency and achieving state-of-the-art accuracy in dynamic scenarios.…”
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Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator by Ahmed M. Abed, Tamer S. Gaafar

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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized optimization for customized production by Chengcai He, Jiaxing Deng, Jingchun Wu, Beicheng Qin, Jinxiang Chen, Yan Li, Qiangsheng Huang

“…The experimental results reveal two key findings: (1) Within the enhanced learning knowledge graph framework, the algorithm—optimized using a graph convolutional network—achieves consistently higher qualification rates across test samples. …”
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Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization by Qiong Bao, Minghao Gao, Jianming Chen, Xu Tan

“…Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. …”
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A Novel Phase Error Estimation Method for TomoSAR Imaging Based on Adaptive Momentum Optimizer and Joint Criterion by Muhan Wang, Silin Gao, Xiaolan Qiu, Zhe Zhang

“…In addition, our approach introduces a joint iterative solution framework, which incorporates a modified accelerated iterative shrinkage-threshold sparse recovery algorithm and an adaptive momentum optimizer for interchannel phase error estimation in a gradient descent manner. …”
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A Framework to Calibrate Ecosystem Demography Models Within Earth System Models Using Parallel Surrogate Global Optimization by Yanyan Cheng, Wei Xia, Matteo Detto, Christine A. Shoemaker

“…In this study, we develop a computationally‐efficient and physical model structure‐based framework that uses a parallel surrogate global optimization algorithm to calibrate ED models. We calibrate two typically coexisted tropical tree species, early and late successional plants, in a state‐of‐the‐art ED model that is capable of simulating successional diversity in forests. …”
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Enhanced Stability and Performance of Islanded DC Microgrid Systems Using Optimized Fractional Order Controller and Advanced Energy Management by Md. Wahidujjaman, Tasnim Ul Bari, Md. Shafiul Alam, Md. Rashidul Islam, Md. Alamgir Hossain, Md. Arafat Hossain, Md. Rafiqul Islam Sheikh

“…To address these issues, this study proposes the use of an optimized fractional order PI (FOPI) controller and an efficient energy management algorithm. …”
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Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes by Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao

“…Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of resampling strategies and classifiers for imbalanced classification tasks. …”
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FastSLAM-MO-PSO: A Robust Method for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments by Xu Bian, Wanqiu Zhao, Ling Tang, Hong Zhao, Xuesong Mei

“…This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. …”
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Two-layer asynchronous distributed optimal voltage control for VSC-HVDC-connected large-scale wind farm clusters by Fuyan Liu, Feifan Shen, Pan Hu, HeSong Cui, Sheng Huang, Ji Zhang, Xia Ma, Kun He

“…The results showed that the proposed scheme could improve the computation efficiency without affecting the original problem’s optimality, while also exhibiting enhanced robustness and environmental adaptability.…”
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Structural Parameter Design of Magnetic Pulse Welding Coil for Dissimilar Metal Joints: Numerical Simulation, Parameter Optimization, and Experiments by Yangfan Qin, Changhui Ji, Hao Jiang, Yuefan Jiang, Junjia Cui, Guangyao Li

“…The optimal Latin hypercube sampling technique (OLHS), Kriging approximate model, and the Non-Linear Programming by Quadratic Lagrangian (NLPQL) algorithm were employed in the optimization procedure, based on the finite element model built in LS-DYNA. …”
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Optimizing the Migratory Environment of Wildebeests in the Maasai Mara Reserve with a New Ecological Corridor and Customized Buffer Zone Model by Xinrui Fan, Kuok Ho Daniel Tang, Shoushuo Liu, Yang Liu, Charles Ken Smith

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Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis by Li Zhang, Jie Yang, Jun Wang, Lening Wang, Haiming Niu, Xiaobing Liu, Simon X. Yang, Kun Yang

“…Subsequently, a regional-voltage-coordinated regulation model is developed using sensitivity analysis, followed by the establishment of a mathematical model, solution algorithm, and operational procedure for multi-station AVC-coordinated response optimization. …”
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Enhanced thyroid nodule detection and diagnosis: a mobile-optimized DeepLabV3+ approach for clinical deployments by Changan Yang, Muhammad Awais Ashraf, Mudassar Riaz, Pascal Umwanzavugaye, Kavimbi Chipusu, Hongyuan Huang, Yueqin Xu

“…Preprocessing steps, including noise reduction and contrast optimization, were applied to enhance image clarity. …”
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An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. …”
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Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron by Yacine Karmi, Haithem Boumediri, Omar Reffas, Yazid Chetbani, Sabbah Ataya, Rashid Khan, Mohamed Athmane Yallese, Aissa Laouissi

“…This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. …”
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Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…Finally, the model’s prediction accuracy is further enhanced by optimizing the hyperparameters of XGBoost through Bayesian optimization (BO) algorithms, resulting in the development of MM–BO–XGBoost models. …”
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Reliability Analysis of High-Pressure Tunnel System Under Multiple Failure Modes Based on Improved Sparrow Search Algorithm–Kriging–Monte Carlo Simulation Method by Yingdong Wang, Chen Xing, Leihua Yao

“…Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. …”
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Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization by Muhammad Hilal, Haider TH. Salim ALRikabi, Ibtisam A. Aljazaery

“… The Generalized Recurrent Neural Network (GRNN) and Invasive Weed Optimization (IWO) algorithms are two powerful techniques that can be used to optimize motor drive speed. …”
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A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals by Priyaranjan Kumar, Prabhat Kumar Upadhyay

“…Abstract In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms have been used in the model’s optimization to improve efficiency and accelerate convergence while extracting features from time and frequency domain EEG data. …”
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