Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning by Liangcai Zhou, Long Huo, Linlin Liu, Hao Xu, Rui Chen, Xin Chen

“…A heterogeneous multi-agent proximal policy optimization (H-MAPPO) DRL algorithm is introduced for multi-area power systems. …”
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Adaptive Production Rescheduling System for Managing Unforeseen Disruptions by Andy J. Figueroa, Raul Poler, Beatriz Andres

“…The approach begins by generating an optimal production plan through batch assignments to machines. …”
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Using the clustering method to find the final environmental parameters coefficients in road construction projects by Eshagh Rasouli Sarabi, Ramin Vafaei Poursorkhabi, Mehdi Ravanshadnia

“…In the first phase, the Genetic Optimization Algorithm was implemented to determine convenient coefficients for the relevant parameters. …”
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Recovery and Characterization of Tissue Properties from Magnetic Resonance Fingerprinting with Exchange by Naren Nallapareddy, Soumya Ray

“…Our results show that Simplicial Homology Global Optimization (SHGO), a global optimization algorithm, and Limited-memory Bryoden–Fletcher–Goldfarb–Shanno algorithm with Bounds (L-BFGS-B), a local optimization algorithm, performed comparably with direct matching in two-tissue property MRF at an SNR of 5. …”
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Improved ant colony algorithm based cloud computing user task scheduling algorithm by Sining LUO, Hualong WANG, Hongyu LI, Wei PENG

“…In recent years,with the development of power information,more and more power applications and tasks are deployed in the cloud.Because of the dynamic heterogeneity of cloud resources and power applications,it is a challenge in the cloud computing system to realize resource division and task scheduling.Power applications need to be able to achieve a rapid response and minimum completion time,and schedulers should consider the load of each cloud computing node to ensure the reliability of cloud computing.A task scheduling algorithm based on the algorithm of improving an ant colony was proposed to solve the problem of task scheduling in virtual machines.Through the improvement of the standard ant colony algorithm,the task scheduling time was reduced and load balancing was realized while minimizing the overall completion time.The results show that the algorithm can shorten the task scheduling time and realize the load balancing of cloud nodes,which provides technical basis for the optimization of power cloud computing.…”
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Comparison of Dynamic Programming Algorithm and Greedy Algorithm on Integer Knapsack Problem in Freight Transportation by Global Ilham Sampurno, Endang Sugiharti, Alamsyah Alamsyah

“…The purpose of this research is to know how to get optimal solution result in solving Integer Knapsack problem on freight transportation by using Dynamic Programming Algorithm and Greedy Algorithm at PT Post Indonesia Semarang. …”
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Strategic Deployment of a Single Mobile Weather Radar for the Enhancement of Meteorological Observation: A Coverage-Based Location Problem by Bikram Parajuli, Xin Feng

“…The proposed location problem is solved optimally using the geometric branch-and-bound algorithm and heuristically using swarm-based optimization algorithms. …”
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A Cooperative GNSS Vector-DLL (CoVDLL) Method for Multiple UAVs Positioning by Chuntao Li, Xinru Wang, Changhui Jiang, Zikang Su, Shoubin Chen, Yuwei Chen

“…To optimize navigation solution estimation in the CoVDLL, a Factor Graph Optimization (FGO) algorithm is employed to realize the navigation solution’s optimal estimation. …”
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Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework by Ling Li, Lidong Zhu, Weibang Li

“…Specifically, (1) an adaptive knowledge-sharing mechanism based on transfer learning is designed to balance device heterogeneity and data distribution divergence through dynamic weighting factors; (2) a bi-level reinforcement learning device selection strategy is proposed, combining meta-learning and hierarchical attention mechanisms to optimize global–local decision-making and enhance model convergence efficiency; (3) dynamic privacy budget allocation and robust aggregation algorithms are introduced to reduce communication overhead while ensuring privacy. …”
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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…In addition, we established predictive models for lung doses utilizing the Least Absolute Shrinkage and Selection Operator, ridge, and linear regression algorithms while also evaluating dosimetric parameters for both the PTV and the organs at risk (OARs). …”
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Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…The results validate the feasibility of DRL–based WPT control, paving the way for scalable, resilient, and self-optimizing wireless power grids on the Moon. Future work will explore the integration of hybrid energy storage models, quantum-inspired optimization for real-time decision-making, and predictive beamforming algorithms to further enhance the reliability and efficiency of lunar energy networks.…”
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SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks by Sneh Pandya, Purvik Patel, Brian D Nord, Mike Walmsley, Aleksandra Ćiprijanović

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The Mode of Constructing Safe Trajectories of Motion of the Unmanned Aerial Vehicle while Monitoring Power Lines Considering the Influence of their Electromagnetic Fields by Shabanova A.R., Tolstoy I.M., Lebedev I.V.

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Distributed dynamic event-triggered time-varying resource management for microgrids via practical predefined-time multiagent methods by Tingting Zhou, Salah Laghrouche, Youcef Ait-Amirat

“…To address this problem, a fully distributed predefined-time (PDT) optimization algorithm is developed, incorporating a time-base generator (TBG) to guarantee convergence within a PDT, independently of initial conditions and system parameters. …”
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Electric Vehicle Routing Problem with Heterogeneous Energy Replenishment Infrastructures Under Capacity Constraints by Bowen Song, Rui Xu

“…Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. …”
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A Particle Swarm Optimization-Based Ensemble Broad Learning System for Intelligent Fault Diagnosis in Safety-Critical Energy Systems with High-Dimensional Small Samples by Jiasheng Yan, Yang Sui, Tao Dai

“…To address the aforementioned challenge, this paper proposes an innovative IFD method for SCES that combines the particle swarm optimization (PSO) algorithm and the ensemble broad learning system (EBLS). …”
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Vehicle-To-Grid (V2G) Charging and Discharging Strategies of an Integrated Supply–Demand Mechanism and User Behavior: A Recurrent Proximal Policy Optimization Approach by Chao He, Junwen Peng, Wenhui Jiang, Jiacheng Wang, Lijuan Du, Jinkui Zhang

“…This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems.…”
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High-Performance Deployment Operational Data Analytics of Pre-Trained Multi-Label Classification Architectures with Differential-Evolution-Based Hyperparameter Optimization (AutoDE... by Teo Prica, Aleš Zamuda

“…In this differential evolution (DE) optimization use case, we analyze how energy efficiently the DE algorithm performs with different DE strategies and ML models. …”
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Revolutionizing Electric Vehicle Charging Stations with Efficient Deep Q Networks Powered by Multimodal Bioinspired Analysis for Improved Performance by Sugunakar Mamidala, Yellapragada Venkata Pavan Kumar, Rammohan Mallipeddi

“…This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). …”
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A Two-Stage Robust Optimization Strategy for Long-Term Energy Storage and Cascaded Utilization of Cold and Heat Energy in Peer-to-Peer Electricity Energy Trading by Yun Chen, Yunhao Zhao, Xinghao Zhang, Ying Wang, Rongyao Mi, Junxiao Song, Zhiguo Hao, Chuanbo Xu

“…The strategy includes a UIES model with a photovoltaic (PV)–green roof, hydrogen storage, and cascading cold/heat energy subsystems. The first stage optimizes energy trading volume to maximize social welfare, while the second stage maximizes operational profit, considering uncertainties in PV generation and power prices. …”
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