Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning by Qingli Liu, Yongjie Ma

“…The simulation results show that the system spectrum efficiency is improved by 19.1% on average compared with the centralized DDPG, MADDPG, MAPPO and FL-DuelingDQN algorithms in the Vehicle Networking scenario, while the transmission success rate of the V2V link is improved by 9.3% on average, and the total capacity of the V2I link is increased by 16.1% on average.…”
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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…Existing tiered storage systems predominantly employ reactive policies that respond to observed access patterns, leading to suboptimal performance under dynamic workloads and failing to address multi-objective optimization requirements. We propose a novel proactive data placement framework that integrates predictive deep learning with multi-objective reinforcement learning to anticipate future data access patterns and optimize placement decisions across storage hierarchies. …”
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Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis. by Venkata Suresh Bonthala, Katie Mayes, Joanna Moreton, Martin Blythe, Victoria Wright, Sean Tobias May, Festo Massawe, Sean Mayes, Jamie Twycross

“…The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. …”
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Coasting-cruising Combined Control Strategy Based on Train Energy-efficient Operation by GAO Hao, CHAI Juan, LING Xiaoque, XU Haigui

“…Multiple control stages are divided according to the line conditions, with maximum traction, coasting, cruising, and maximum braking as control inputs for each stage. The genetic algorithm is used to calculate respectively the train energy-efficient optimized operation curves under the coasting-cruising combined control strategy and the multiple coasting control strategy. …”
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Deep Reinforcement Learning for Dynamic Pricing and Ordering Policies in Perishable Inventory Management by Yusuke Nomura, Ziang Liu, Tatsushi Nishi

“…To tackle this challenge, we propose eliminating irrational actions in dynamic programming without sacrificing optimality. To solve this problem more efficiently, we also implement a deep reinforcement learning algorithm, proximal policy optimization, to solve this problem. …”
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Modeling the Stability of the Housing and Construction Complex Based on Organizational and Economic Interaction of Entities by N. N. Shelomentseva, O. V. Grushina

“…A genetic algorithm in MATLAB was used for this purpose. Acceptable options for all participants were selected from a set of Pareto-optimal alternatives based on the preferences of the decision-maker. …”
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MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction by Haoran Wang, Haijun Liu, Jing Yuan, Huijun Le, Weifeng Shan, Liangchao Li

“…It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. …”
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Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images by Zhendong Ji, Lingzhi Yin, Jinhong Wang

“…Principal Component Analysis (PCA) was applied to reduce feature dimensionality, and the Random Forest classification algorithm was optimized with Bayesian Optimization and Tree-structured Parzen Estimators (TPE) for improved performance. …”
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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

“…Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. …”
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Large-Language-Model-Enabled Text Semantic Communication Systems by Zhenyi Wang, Li Zou, Shengyun Wei, Kai Li, Feifan Liao, Haibo Mi, Rongxuan Lai

“…Based on this, we derive the optimal decoding criteria for the receiver and introduce beam search algorithm to further reduce complexity. …”
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Research on Parameter Distribution Features of Photovoltaic Array under the Cover and Shadow Shading Conditions by Honglu Zhu, Cao Yu, Lingxing Lu, Weiwei Lian, Jianxi Yao, Yang Hu

“…Shadowing decreases the optimal operating voltage while covering leads to a decrease in the optimal operating voltage and the open-circuit voltage. …”
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Multi-Inverter Synchronization and Dynamic Power Allocation via Distributed Communication for PV Station Rapid Power Control to Enhance Power System Stability by Mingkang Wu, Min Cheng, Jiawei Yu, Yayao Zhang, Yuanfu Zhu, Yihua Zhu

“…A dynamic power allocation algorithm, embedded with adaptive droop characteristics, optimizes active power distribution across inverters while minimizing transient overshoot. …”
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Microscopic stress-constrained two-scale topology optimisation for additive manufacturing by Xiaopeng Zhang, Zheng Ni, Akihiro Takezawa, Yaguang Wang, Zhan Kang

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Development of the design of mobile solar power plant by P. I. Savelov, Yu. E. Livshic

“…The authors have developed the algorithm for the operation of a mobile solar power plant and the design of the control unit for the solar cell positioning system. …”
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Trajectory Planning Method for Formation Rendezvous of Underactuated Multi-UUV Under Multiple Constraints by Qingzhe Wang, Da Xu, Xiaoran Liu, Gengshi Zhang, Zhao Han

“…By integrating constraints into the DPPSO algorithm’s fitness function, the boundary conditions of the polynomial trajectories are iteratively optimized to derive trajectories that satisfy all constraints. …”
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Fine-Tuning Quadcopter Control Parameters via Deep Actor-Critic Learning Framework: An Exploration of Nonlinear Stability Analysis and Intelligent Gain Tuning by Hassan Moin, Umer Hameed Shah, Muhammad Jawad Khan, Hasan Sajid

“…This study implements an optimal gain self-tuning framework for the altitude, attitude, and position controllers of a 6 degrees-of-freedom nonlinear drone system using a deep reinforcement learning algorithm with continuous observation and action spaces. …”
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Numerical Solution of the Inverse Thermoacoustics Problem Using QFT and Gradient Method by Syrym E. Kasenov, Aigerim M. Tleulesova, Almas N. Temirbekov, Zholaman M. Bektemessov, Rysbike A. Asanova

“…The inverse problem was reduced to an optimization problem, where the objective function was minimized using gradient methods, including the accelerated Nesterov algorithm. …”
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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine by Jichang Peng, Ya Gao, Lei Cai, Ming Zhang, Chenghao Sun, Haitao Liu

“…A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. …”
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Investigation of Load Sharing and Dynamic Load Characteristics of a Split Torque Transmission System with Double-Helical Gear Modification by Xuan Liu, Zongde Fang, Haitao Jia, Ning Zhao, Yunbo Shen, Hui Guo, Xijin Zhang

“…The modified tooth surface of a third-stage double-helical gear is obtained by optimizing the amplitude of static loaded transmission error and meshing-in impact via nondominated sorting genetic algorithm-II (NSGA-II). …”
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Phase Error Correction in Sparse Linear MIMO Radar Based on the Equivalent Phase Center Principle by Wenyuan Shao, Jianmin Hu, Yicai Ji, Jun Pan, Guangyou Fang

“…Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. Array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and complexity of imaging algorithms. …”
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