Parameter Estimation for Three-Phase Induction Motor Based on Performance Curves and Differential Evolution by Marcelo F. Castoldi, Gustavo Henrique Bazan, Wesley A. Souza, Clayton L. Graciola, Rafael M. Silva, Alessandro Goedtel

“…The results demonstrate that the proposed method provides accurate parameter estimation while reducing computational effort, establishing it as a low-cost and practical alternative for industrial applications requiring reliable motor modeling and control optimization.…”
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Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems by Sultan Refa Alotaibi, Hanan Abdullah Mengash, Mohammed Maray, Faiz Abdullah Alotaibi, Abdulwhab Alkharashi, Ahmad A. Alzahrani, Moneerah Alotaibi, Mrim M. Alnfiai

“…This study proposes an Osprey Optimization Algorithm with Deep Learning Assisted Crowd Density Detection and Classification (OOADL-CDDC) technique for smart video surveillance systems. …”
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Energy optimization through morphing blade design under structural constraints: a case study on the NREL 1.5 MW wind turbine by Najafian Arezoo, Jahangirian Alireza

“…The morphing process is modeled using an m-degree shape function and optimized through a Genetic Algorithm (GA) to maximize power generation while minimizing structural displacement and thrust forces. …”
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Optimal Driving Torque Control Strategy for Front and Rear Independently Driven Electric Vehicles Based on Online Real-Time Model Predictive Control by Hang Yin, Chao Ma, Haifeng Wang, Zhihao Sun, Kun Yang

“…Active slip control is applied when slip rates exceed critical thresholds, while under normal conditions, torque distribution is optimized to minimize energy losses. …”
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Optimized Dual-Battery System with Intelligent Auto-Switching for Reliable Soil Nutrient Monitoring in Remote IoT Applications by Doan Perdana, Pascal Lorenz, Bagus Aditya

“…The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA)—tailored to maximise sensor operational longevity. …”
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Development of A Novel Discharge Routing Method Based On the Large Discharge Dataset, Muskingum Model, Optimization Methods, and Multi-Criteria Decision Making by Mahdi Valikhan Anaraki, Saeed Farzin, Iman Ahmadianfar, Amin Shams

“…Different MOAs, including a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Shark Smell Optimization (SSO), Whale Optimization Algorithm (WOA), Harris Hawk''s Optimization (HHO), and hybrid of WOA and CS (WOA_CS), were developed for Muskingum calibration. …”
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Experimental study on solution possibilities of multiextremal optimization problems through heuristic methods by Rudolf A. Neydorf, Ivan V. Chernogorov, Orkhan Takhir Yarakhmedov, Victor V. Polyakh

“…Therefore, three best known and developed search engine optimization techniques are studied: particle swarm method, evolutionary genetic approach, and ant colony algorithm. …”
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Designing High‐Performance, Manufacturing‐Friendly Rotor Blades for Micro Wind Turbines via Cambered Plate Airfoil Optimization by Moritz Burmester, Abdullah Khisraw, Peter Dalhoff

“…Aerodynamic performance was analyzed using XFOIL, with evaluations conducted at Reynolds numbers of 30,000, 40,000, and 50,000 to ensure robust performance across realistic operating scenarios. The iterative optimization employed both single‐objective and (genetic) multi‐objective algorithms, targeting both aerodynamic efficiency and manufacturability. …”
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The open-closed mod-minimizer algorithm by Ragnar Groot Koerkamp, Daniel Liu, Giulio Ermanno Pibiri

“…Abstract Sampling algorithms that deterministically select a subset of $$k$$ k -mers are an important building block in bioinformatics applications. …”
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An Effective Genetic Algorithm for Mixed Precision by Wanyu Zhang, Yu Shang, Min Tsao, Yiwei Li, Xiaoyu Song

“…Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. …”
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Modeling and Performance Evaluation of Hybrid Classical–Quantum Serverless Computing Platforms by Claudio Cicconetti

“…While quantum computing technologies are evolving toward achieving full maturity, hybrid algorithms, such as variational quantum computing, are already emerging as valid candidates to solve practical problems in fields, such as chemistry and operations research. …”
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PERFORMANCE PREDICTION OF ROADHEADERS USING SUPPORT VECTOR MACHINE (SVM), FIREFLY ALGORITHM (FA) AND BAT ALGORITHM (BA) by Arash Ebrahimabadi, Alireza Afradi

“…It can be concluded that while predictive models produce satisfactory results, the Bat Algorithm (BA) demonstrates a higher level of precision and realism in its outcomes.…”
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A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms by Jorge Paredes, Danilo Chávez, Ramiro Isa-Jara, Diego Vargas

“…Additionally, the hybrid approach enables early predictions of approximately 17 cycles and late predictions of only 2%, while other learning algorithms, such as linear regression, do not make early predictions but 100% of their predictions are late. …”
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Novel Application of Quantum Computing for Routing and Spectrum Assignment in Flexi-Grid Optical Networks by Oumayma Bouchmal, Bruno Cimoli, Ripalta Stabile, Juan Jose Vegas Olmos, Carlos Hernandez, Ricardo Martinez, Ramon Casellas, Idelfonso Tafur Monroy

“…By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, we employ the Quantum Approximate Optimization Algorithm (QAOA) to effectively solve it. …”
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The Optimization of Supply–Demand Balance Dispatching and Economic Benefit Improvement in a Multi-Energy Virtual Power Plant within the Jiangxi Power Market by Tang Xinfa, Wang Jingjing, Wang Yonghua, Wan Youwei

“…The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. …”
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A novel Parrot Optimizer for robust and scalable PEMFC parameter optimization by Mohammad Aljaidi, Pradeep Jangir, Arpita, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Gulothungan G., Ali Fayez Alkoradees, Mohammad Khishe, Reena Jangid

“…I–V and V–P characteristic simulations match experimental results across different temperature and pressure values which proves the theoretical value and practical usage of PO in solving nonlinear optimization problems. The study demonstrates PO as a dependable optimization method which improves PEMFC design processes while enhancing operational reliability through future research that includes real-time control and algorithm combination and system scalability.…”
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A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting by Iman Ahmadianfar, Aitazaz Ahsan Farooque, Mumtaz Ali, Mehdi Jamei, Mozhdeh Jamei, Zaher Mundher Yaseen

“…Hence, a novel hybrid model is provided, incorporating singular value decomposition (SVD) in conjunction with kernel-based ridge regression (SKRidge), multivariate variational mode decomposition (MVMD), and the light gradient boosting machine (LGBM) as a feature selection method, along with the Runge–Kutta optimization (RUN) algorithm for parameter optimization. …”
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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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Aerodynamic Optimization of Morphing Airfoil by PCA and Optimization-Guided Data Augmentation by Ao Guo, Jing Wang, Miao Zhang, Han Wang

“…A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. …”
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Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure by Naba Raj Khatiwoda, Babu Ram Dawadi, Sashidhar Ram Joshi

“…We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. …”
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