Green Technology Game and Data-Driven Parameter Identification in the Digital Economy by Xiaofeng Li, Qun Zhao

“…These findings offer theoretical guidance for optimizing green policy design and identifying behavioral pathways, while establishing a foundation for data-driven modeling of dynamic evolutionary processes.…”
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Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field by HAN Yanling, LYU Jinke, ZHOU Guofeng, ZHANG Like, ZHANG Yun

“…The proposed method emploied the grey wolf optimization algorithm to adaptively adjust the key parameters of the hybrid A*(HA*) algorithm, achieving a multi-objective balance among path length, smoothness, and planning time. …”
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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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Upgrading fuzzy logic by GA-PS to determine asphaltene stability in crude oil by Saeid Ahmadi, Mohammad Sadegh Amiribakhtiar, Amin Gholami, Nader Bahrami

“…While SC solely optimizes mean of input Gaussian membership functions (GMFs), GA-PS tool optimizes both mean and variance of input GMFs. …”
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Distributed Task Allocation for UAV Swarms with Limited Communication by Asım Egemen Yılmaz, Mutullah Eşer

“…Task allocation, a crucial aspect of UAV swarm autonomy, involves assigning sequential tasks to each aircraft based on environmental constraints and swarm status. While many task allocation algorithms assume reliable communication among agents, real-world environments often present challenges such as limited bandwidth and message interference. …”
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Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorith... by S. Ajith, S. Vijayakumar, N. Elakkiya

“…Artificial intelligence algorithms efficiently process vast datasets from unmanned aerial vehicles, ground vehicles, and satellites, enabling precise and timely interventions. …”
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Kinematic analysis and optimisation of a gantry spraying robot for ship blocks by Jiangmin Xu, Jinhao Liu, Xuwen Jing, Honggen Zhou, Xiaocai Hu, Jianbo Ji, Zhenchun Han

“…The differential evolution algorithm and the adaptive differential evolution algorithm were both utilized to solve the optimal inverse solution. …”
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Distributed Robust Low-Carbon Economic Dispatch of Power Systems Considering Extreme Scenarios by HU Heng, QIN Jianru, LI Haibo, LIU Jiefeng

“…[Methods] To address this， this paper first constructs RE generation scenarios using Latin hypercube sampling （LHS） and modified k-means clustering， verifying their reserve feasibility， while transforming reserve-infeasible scenarios into extreme scenario sets. …”
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Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression by LIANG Zhikai, ZHANG Zhichao, HU Lan, PANG Qiu

“…The maximum error between the four groups of tests and simulations is 4.24%， while the maximum error with the actual forming results is -2.45%.…”
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Early Detection of Voltage Instability: A Transparent, Rule-Based Method for Cyber-Resilient Grids by Mahtab Khalilifar, Seyed Mohammad Shahrtash

“…Optimized for real-time operation, the simulation-based algorithm meets stringent computational speed requirements for online stability assessment. …”
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Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods by Wenchao Li, Houmin Li, Cai Liu, Kai Min

“…Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. …”
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Ship Typhoon Avoidance Route Planning Method Under Uncertain Typhoon Forecasts by Zhengwei He, Junhong Guo, Weihao Ma, Jinfeng Zhang

“…The experimental results demonstrate that under adverse conditions of uncertain typhoon forecasts, the proposed multi-objective typhoon avoidance route optimization model can effectively avoid high wind and wave areas of the typhoon while balancing and optimizing multiple navigation indicators. …”
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Analysis of Unmanned Surface Vehicles Heading KF-Based PI-(1+PI) Controller Using Improved Spider Wasp Optimizer by Xiaoyu Li, Xiangye Zeng, Jingyi Wang, Qi Li, Baoshuo Fan, Qi Zeng

“…A transfer function model of the USV heading system is established using voyage data, with ISWO optimizing its parameters, achieving a 5.67% reduction in mean squared error (MSE) compared to the original Spider Wasp Optimizer and outperforming classical algorithms like Arithmetic Optimization Algorithm (AOA), Crayfish Optimization Algorithm (COA), and Marine Predators Algorithm (MPA). …”
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Volumetric Patch-Based Super-Resolution Reconstruction of Hyperpolarized ¹³C Cardiac MRI by Sung-Han Lin, Junjie Ma, Jae Mo Park

“…This study is to develop and characterize a volumetric patch-based super-resolution reconstruction algorithm that enhances spatial resolution 13C cardiac MRI by utilizing structural information from 1H MRI. …”
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Rapid differentiation of patients with lung cancers from benign lung nodule based on dried serum Fourier-transform infrared spectroscopy combined with machine learning algorithms by Huanyu Li, Lixue Dai, Shaomei Guo, Hongluan Wang, Lei Lei, Jie Yu, Xiaoyun Li, Jun Wang

“…All algorithms successfully differentiated the three groups, with LDA achieving the highest accuracy (93.9 %). …”
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Performance Comparison of Multi-Objective Optimizers for Dynamic Balancing of Six-Bar Watt Linkages Using a Fully Cartesian Model by María T. Orvañanos-Guerrero, Claudia N. Sánchez, Luis Eduardo Robles-Jiménez, Sara Carolina Gómez-Delgado

“…A wide set of optimization methods is analyzed and compared, and among them, the S-Metric Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) demonstrates superior performance. …”
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Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests by Thuy Phuong Nguyen, Phuc Khoa Nguyen, Huu Ngu Nguyen, Thanh Duc Tran, Gia Tung Pham, Thai Hung Le, Dinh Huy Le, Trung Hai Nguyen, Van Binh Nguyen

“…The study identified the optimal model for estimating forest aboveground biomass and minimizing the number of input variables while achieving satisfactory model performance. …”
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Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks by Dinh-Hieu Tran, Symeon Chatzinotas, Bjorn Ottersten

“…In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV’s resource allocation, and trajectory while cache capacity and flight time are limited. …”
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Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems by Shan Tao, Lei Yang, Xiaobo Zhang, Shengya Zhao, Kun Liu, Xinran Tian, Hengxin Xu

“…Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings.…”
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Blockchain-based heterogeneous resource configuration scheme in computing power network by Qiang Gao, Chunyu Liu, Lei Wang, Yiqing Liu, Yueqiang Xu

“…Given the complexity of the original problem, we decompose it into two subproblems and solve each using the Lagrange multiplier iterative algorithm. To validate the effectiveness of our proposed solution, we compare it with several baseline approaches, including those based on Particle Swarm Optimization (PSO) and reinforcement learning methods such as Proximal Policy Optimization (PPO). …”
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