Monitoring of Glacier Area Changes in the Ili River Basin during 1992–2020 Based on Google Earth Engine by Qinqin Zhang, Zihui Zhang, Xiaofei Wang, Zhonglin Xu, Yao Wang

“…Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. …”
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A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction by Xu He, Zhengpu Wu, Jinghan Bai, Junchao Zhu, Lu Lv, Lujun Wang

“…With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. …”
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Mixed Time/Event-Triggered Model Predictive Tracking Control for Networked Mobile Robots by Huixin Liu, Yonghua Lai, Hongsong Lian, Guobin Wang, Dongsheng Zheng

“…The MPC algorithm is developed based on the auxiliary optimization problem (OP), which is constrained by linear matrix inequalities. …”
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FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer by Dongyan Wang, Limin Chen, Xiaotong Lu, Yidi Wang, Yue Shen, Jingjing Xu

“…This paper proposes a federated learning approach based on the dung beetle optimizer, named FedDBO. In this method, the model parameters uploaded from clients to the server are transformed into model scores. …”
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Optimization Study of Multidimensional Computational Model for Distribution Network Cable Fault Location and Hidden Danger Warning System in Smart Grid Environment by Xin Li, Jiyou Wang, Minghui Liu

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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…Among the machine learning methods used for modelling, the prediction results of the tree model are improved to a certain degree after the error-correcting output code optimization. …”
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A VNS-NPGA approach to multi-objective optimization of hub-and-spoke logistics network by Changxi Ma, Chuwei Shi, Yun Yang, Yongpeng Zhao, Zhuye Xu, Bo Du

“…To realize low-cost freight transport in the logistics network and improve the network operation efficiency, a multi-objective optimization model and the corresponding algorithm for a hub-and-spoke logistics network are proposed based on the multi-level location of hub points and channels layout. …”
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Balanced operation strategies of district heating systems based on dynamic hydraulic-thermal modeling by Xiaojie Lin, Ning Zhang, Zheng Luo, Encheng Feng, Wei Zhong

“…For the primary side network in the various operation conditions, the dynamic hydraulic-thermal model is established. The heating supply imbalance of the primary side return temperatures among different heating substations is defined and optimized based on the dynamic hydraulic-thermal model and the particle swarm optimization algorithm. …”
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Spatially optimized allocation of water and land resources based on multi-dimensional coupling of water quantity, quality, efficiency, carbon, food, and ecology by Yingbin Wang, Haiqing Wang, Jiaxin Sun, Peng Qi, Wenguang Zhang, Guangxin Zhang

“…The COM-WL model integrates our improved genetic algorithm, PAEA-NSGAⅢ, with the landscape allocation model, GridLOpt. …”
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An Enhanced Generative Adversarial Network Prediction Model Based on LSTM and Attention for Corrosion Rate in Pipelines by Pujun Long, Mi Liang, Hongjian Chen, Qin Yang

“…To address the pervasive issue of internal pipeline corrosion in the oil and gas industry, this paper proposes a hybrid intelligent model for predicting corrosion rates. This model integrates an improved Generative Adversarial Network with Grey Wolf Optimization and Support Vector Regression (LAGAN-GWO-SVR). …”
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Optimized fiducial marker placement using B‐spline surface modeling and graph theory for Cyberknife stereotactic body radiotherapy for superficial tumors by Jing Huang, Xianlong Xiong, Cheng Chen, Yuhan Li, Ruijie Wang, Zhitao Dai

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Game algorithm based on link quality: Wireless sensor network routing game algorithm based on link quality by Zhanjun Hao, Jiaojiao Hou, Jianwu Dang, Xiaochao Dang, Nanjiang Qu

“…In the simulation experiment, the influence of the change of link quality parameters on the performance of the algorithm is analyzed, and the proposed algorithm is compared with non-linear weight particle swarm optimization (NWPSO) algorithm and Low Energy Adaptive Clustering Hierarchy-Improvement (LEACH-IMPT) algorithm in three aspects: the number of surviving nodes, network lifetime, and network energy consumption. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

“…However, the computational overhead in training models and identifying optimal training hyperparameters are challenging problems. …”
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The open-closed mod-minimizer algorithm by Ragnar Groot Koerkamp, Daniel Liu, Giulio Ermanno Pibiri

“…Abstract Sampling algorithms that deterministically select a subset of $$k$$ k -mers are an important building block in bioinformatics applications. …”
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Application of a grey wolf optimization-enhanced convolutional neural network and bidirectional gated recurrent unit model for credit scoring prediction. by Yetong Fang

“…CNN performs well in feature extraction and can effectively capture patterns in customer historical behaviors, while BiGRU is good at handling time dependencies, which further improves the prediction accuracy of the model. The GWO algorithm is introduced to further improve the overall performance of the model by optimizing key parameters. …”
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Optimized Dual-Battery System with Intelligent Auto-Switching for Reliable Soil Nutrient Monitoring in Remote IoT Applications by Doan Perdana, Pascal Lorenz, Bagus Aditya

“…To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. …”
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Tailoring orthotropic mechanical and thermal properties in nonlinear contact, vibration and thermal problems via kriging-based optimization by Yicong Zhou, Jinyan Duan, Guoxin Mao, Jun Hong

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Control parameter identification method for virtual synchronous generator considering electric vehicles based on dynamic particle swarm optimization by Dongqi Wu, Yong Hu

“…By constructing a VSG inverter control model suitable for distributed power sources and EV charging systems, analyzing the interactions between active and reactive power control loops under EV integration scenarios, selecting parameters and observations to be identified, and improving the Particle Swarm Optimization (PSO) algorithm based on actual conditions, the method ensures enhanced system adaptability. …”
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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

“…The results indicated that BO hyperparameter optimization is the most effective, providing the model with the best performance and higher prediction accuracy than the other two optimization algorithms. …”
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Research on fault diagnosis of amorphous alloy transformers by using vibration signals and a PSO-optimized full-process WPT-SVM model by Daosheng Liu, Wentao Yang, Longsheng Liu, Zhe Zhao

“…Overall, the full-process optimization significantly improves AMT fault diagnosis efficiency compared with single-aspect optimization.…”
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