Power Control and Voltage Regulation for Grid-Forming Inverters in Distribution Networks by Xichao Zhou, Zhenlan Dou, Chunyan Zhang, Guangyu Song, Xinghua Liu

“…An enhanced whale optimization algorithm (EWOA) is designed to complete the algorithm solution, thereby achieving the optimal system configuration, where an improved attenuation factor and position updating mechanism is proposed to enhance the EWOA’s global optimization capability. …”
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A Novel Deep Learner for Human Behavior Prediction Over Public Video Surveillance by Bayan Alabdullah, Bisma Batool Fatima, Haifa F. Alhasson, Mohammed Alshehri, Yahya AlQahtani, Nouf Albhassabi, Hui Liu

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Enhanced Transmission Expansion Planning With a High Confidence Strategy Considering Fluctuations in Solar PV Generation and Electricity Demand by Siripat Somchit, Niphon Kaewdornhan, Rongrit Chatthaworn

“…To address this issue, this work proposes an enhanced TEP-based 24-hour situation optimized by an improved Binary Differential Evolution (BDE) algorithm assisted by the logistic map for adjusting the mutation factor and crossover rate. …”
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An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation by Rui Yang, Yongbao Liu, Xing He, Zhimeng Liu

“…When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. …”
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Balancing line hardening, distributed generation and de-energization for wildfire risk mitigation with microgrid formation by Mengqi Yao, Shunbo Lei, Weimin Wu, Duncan S. Callaway

“…An adopted column-and-constraint generation algorithm is developed to solve the model and obtain the optimal decisions. …”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…Future work will explore dynamic parameter optimization and algorithm adaptability across diverse datasets to further refine its capabilities. …”
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Generative Adversarial and Transformer Network Synergy for Robust Intrusion Detection in IoT Environments by Pardis Sadatian Moghaddam, Ali Vaziri, Sarvenaz Sadat Khatami, Francisco Hernando-Gallego, Diego Martín

“…Additionally, an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm is employed to fine-tune the hyper-parameters of the hybrid model, further enhancing learning stability and detection performance. …”
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Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means by Bo Li, Guochao Qian, Lijun Tang, Peng Sun, Zhensheng Wu

“…Firstly, the DBPSO algorithm is used to optimize the configuration scheme of the distributed voltage and current sensing device, which reduces the number of measuring devices and system cost on the premise of ensuring the global observability of the distribution network. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

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RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning by Yang Jing, Li Weiya

“…These methods have high computational costs and are not efficient for real-time applications.MethodsTo address these issues, this paper presents a Quantum-behaved Particle Swarm Optimization model enhanced by deep reinforcement learning (RL-QPSO Net) aimed at improving global optimality and adaptability in path planning. …”
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Response mitigations of adjacent structure with MPTMD under real and stochastic excitations by Mohammad Alibabaei Shahraki

“…The performance of the MPTMD system is optimized using the Particle Swarm Optimization (PSO) algorithm. …”
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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

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DRL-Leveraged and RIS-Assisted Hybrid Network Slicing for eMBB and URLLC Co-Existence in 6G Systems by Bhagawat Adhikari, Ahmed Shaharyar Khwaja, Muhammad Jaseemuddin, Alagan Anpalagan

“…Similarly, there is an improvement of 78.36% in execution time compared to the SOTA URLLC allocation technique based on the heuristic algorithm.…”
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Stackelberg Game Based on Trajectory Prediction for Lane Change in Mixed Traffic by Baichuan Shi, Li Zhai, Chang Liu

“…The method develops a utility function for human-driven vehicles incorporating driving styles and safety-comfort-efficiency factors, with a corresponding cost function for autonomous vehicles. An improved Stackelberg game model integrates trajectory prediction of human-driven vehicles, while a bi-level optimization algorithm combining model predictive control and genetic algorithms jointly optimizes acceleration sequences and lane-change timing. …”
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Wind Turbine Stall‐Induced Aeroelastic Instability Mitigation Using Vortex Generators by Qingshen Meng, Chao Chen, Xugang Hua, Wei Yu

“…ABSTRACT Inspired by Vortex Generators' success in delaying airfoil stall, this study explores the potential of using Vortex Generators to mitigate stall‐induced instability in floating offshore wind turbines at parked and skewed inflow conditions for the first time. Significant improvements are achieved by strategically installing Vortex Generators in the outboard sections of turbine blades and optimizing their parameters (normalized height, length, inflow angle, and chordwise positions) using the particle swarm optimization algorithm and fast optimization method. …”
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Robust dispatch for electrical–thermal combined smart building considering impacts of uncertainties on thermal side by Weijie He, Fanrong Wei, Xiangning Lin, Samir M. Dawoud

“…The robust scheduling model proposed in this paper that considers the uncertainty on both sides jointly improves the conservatism of traditional scheduling schemes while achieving better economic benefits.…”
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Calculation method of line loss rate of substation areas considering tidal current variation with photovoltaic power generation access by Shengbo Sun, Chao Cheng, Shujun Ji, Wei Cui, Wei Ge, Shifang Hao

“…The proposed method employs an improved K-medoids clustering algorithm for substation area classification, optimized by an enhanced Cuckoo algorithm to minimize classification errors. …”
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Analysis of the impact of mobile robots on the efficiency of warehousing and transport processes in modern textile manufacturing by Karabegović Isak

“…AMRs leverage advanced technologies such as LiDAR sensors, SLAM algorithms, artificial intelligence, and IoT systems to navigate complex industrial environments autonomously, optimize routes, and execute tasks without direct human intervention. …”
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Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning by Jun Tan, Raoof Mohammed Radhi, Kimia Shirini, Sina Samadi Gharehveran, Zamen Parisooz, Mohsen Khosravi, Hossein Azarinfar

“…The proposed framework was evaluated through extensive simulations in a MATLAB environment, where it demonstrated remarkable improvements in system performance. The integration of Digital Twins allowed for precise real-time modeling of system behavior, while Deep Learning algorithms effectively identified and predicted faults. …”
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Machine learning prediction of permeability distribution in the X field Malay Basin using elastic properties by Zaky Ahmad Riyadi, John Oluwadamilola Olutoki, Maman Hermana, Abdul Halim Abdul Latif, Ida Bagus Suananda Yogi, Said Jadid A. Kadir

“…Several ensemble-based models, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightBoost), Categorical Gradient Boosting (CatBoost), Bagging Regressor, Random Forest and Stacking, were evaluated for predictive performance, along with Multi-Layered Perceptron Neural Network algorithms. Additionally, Tree-structured Parzen Estimator-Bayesian optimization method was utilized to optimize the machine learning model's hyperparameters and improve accuracy. …”
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