Comparative Evaluation of Traditional and Advanced Algorithms for Photovoltaic Systems in Partial Shading Conditions by Robert Sørensen, Lucian Mihet-Popa

“…This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). …”
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Estimating the transpiration of kiwifruit using an optimized canopy resistance model based on the synthesis of sunlit and shaded leaves by Zongyang Li, Lu Zhao, Zhengxin Zhao, Huanjie Cai, Liwen Xing, Ningbo Cui

“…This study established a rc estimation model based on a synthesis of sunlit and shaded leaves (SSL) and optimized it using Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). …”
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A Novel, Self-Adaptive, Multiclass Priority Algorithm with VM Clustering for Efficient Cloud Resource Allocation by Hicham Ben Alla, Said Ben Alla, Abdellah Ezzati, Abdellah Touhafi

“…This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. …”
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FHQ-RRT*: An Improved Path Planning Algorithm for Mobile Robots to Acquire High-Quality Paths Faster by Xingxiang Dong, Yujun Wang, Can Fang, Kemeng Ran, Guohui Liu

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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…[Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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Mechanical Impedance Control in the Human Arm While Manually Transporting an Open-Top Fluid Filled Dish by Navit Roth, Rami Seliktar, Joseph Mizrahi

“…The tested subjects walked at constant speed on a treadmill while aiming to minimize liquid spillage from the cup. …”
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Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition by Hang Yu, Junbo Lei, Pengfei Lin, Tao Zhang, Hailong Liu, Huilin Lai, Lindong Lai, Bowen Zhao, Bo Wu

“…This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. …”
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Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses by da Pan

“…Experimental results demonstrate that SA achieves superior performance compared to genetic algorithms (GA), greedy approaches, ant colony optimization (ACO), and particle swarm optimization (PSO). …”
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Assessing generalizability of Deep Reinforcement Learning algorithms for Automated Vulnerability Assessment and Penetration Testing by Andrea Venturi, Mauro Andreolini, Mirco Marchetti, Michele Colajanni

“…To this purpose, we define a novel VAPT environment through which we devise multiple evaluation scenarios. While evidencing the limited capabilities of shallow RL approaches, we consider three state-of-the-art deep RL agents, namely Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor–Critic (A2C), and use them as bases for VAPT operations. …”
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Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems by Sichen Tao, Yifei Yang, Ruihan Zhao, Hiroyoshi Todo, Zheng Tang

“…Therefore, metaheuristic algorithms with inherent discrete characteristics like genetic algorithms (GAs) and particle swarm optimization (PSO) have been extensively developed into current state-of-the-art WFLOP optimizers. …”
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Association Analysis of Benzo[<i>a</i>]pyrene Concentration Using an Association Rule Algorithm by Minyi Wang, Takayuki Kameda

“…Principal component analysis confirmed the robustness and accuracy of our optimized association rule approach in quantifying complex, nonlinear relationships, while providing granular, interpretable insights beyond the traditional methods.…”
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Smart Energy Strategy for AC Microgrids to Enhance Economic Performance in Grid-Connected and Standalone Operations: A Gray Wolf Optimizer Approach by Sebastian Lobos-Cornejo, Luis Fernando Grisales-Noreña, Fabio Andrade, Oscar Danilo Montoya, Daniel Sanin-Villa

“…To assess performance, 100 independent runs per method were conducted, comparing GWO against particle swarm optimization (PSO) and genetic algorithms (GAs). Statistical analysis confirmed that GWO achieved the lowest operational costs (USD 3299.39 in grid-connected mode and USD 11,367.76 in islanded mode), the highest solution stability (0.19% standard deviation), and superior voltage regulation. …”
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Hierarchical Deep Learning Model Optimization Using Enhanced Evolutionary-based Approach for Fake News Detection by Deepti Nikumbh, Anuradha Thakare

“…This work introduces the Deep Learning Model with Evolutionary Computing Approach (DLECA), a novel method for compressing and optimizing hierarchical deep learning models (HDLM). …”
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Design of Hybrid Renewable Energy Systems: Integrating Multi‐Objective Optimization Into a Multi‐Criteria Decision‐Making Framework by Tebello Ntsiki Don Mathaba, Khaled Abo‐Al‐Ez

“…A combination of the MO particle swarm optimization and a newly developed MO leaders‐and‐follower algorithms (MO‐LaF/PSO) is used to generate optimal configurations based on minimal levelized cost of energy (LCOE), renewable energy (RE) power abandonment, and CO2 emissions, while maintaining an acceptable level of reliability. …”
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Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization by Muhammad Umar, Muhammad Farooq Siddique, Niamat Ullah, Jong-Myon Kim

“…The genetic algorithm (GA) is used to optimize feature selection and ensure the selection of the most relevant features to further improve the model’s performance. …”
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Knowledge Graph-Enhanced Digital Twin Framework for Optimized Job Shop Scheduling in Smart Manufacturing by Nehal Tarek, Abeer D. Algarni, Nancy A. El-Hefnawy, Hatem Abdel-Kader, Amira Abdelatey

“…The first algorithm is a genetic algorithm (GA) that minimizes the makespan required to complete the job list for each mode of operation followed by a grey wolf optimization algorithm (GWO) to verify and enhance the results of the GA. …”
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A hybrid GA-SA resource allocation scheme enhanced with SINR optimization for NOMA-MIMO systems in 5G networks by Murad Halabouni, Mardeni Roslee, Sufian Mousa Ibrahim Mitani, Osama M.S. Abujawa, Anwar Faizd Osman, Fatimah Zaharah Ali

“…Genetic Algorithm (GA) optimizes user pairing by evolving high-throughput configurations, while Simulated Annealing (SA) refines power allocation to maximize Signal-to-Interference-plus-Noise Ratio (SINR) and minimize interference. …”
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An improvement in the design process of sustainable peak power rating transformer for solar utility by Emir Yükselen, Ebrahim Rahimpour

“…Such upgrades are essential for transitioning to a zero-emission electricity system and developing green energy projects.In this paper, a transformer has been studied using a combination of electrical design and 3D finite element method simulation to evaluate various design parameters. An optimization study has been conducted using an innovative multi-objective genetic algorithm utilizing a cost function that factors in size and material costs to identify the most efficient and cost-effective design solutions.The proposed design method was then validated through thermal model simulations and experimental tests based on the photovoltaic load cycle. …”
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Version [2.0] - [VIC-Borg: Multiobjective automatic calibration toolkit for VIC model] by Jinfeng Ma, Hua Zheng, Ruonan Li, Kaifeng Rao, Yanzheng Yang, Weifeng Li

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Advanced skin cancer prediction with medical image data using MobileNetV2 deep learning and optimized techniques by Tuğçe Öznacar, Nuray Varol Kayapunar

“…These results underscore the potential of deep learning models, optimized with the memetic algorithm, to significantly improve skin cancer detection. …”
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