White Shark Optimization for Solving Workshop Layout Optimization Problem by Bin Guo, Yuanfei Wei, Qifang Luo, Yongquan Zhou

“…Using a real–world case study, the White Shark Optimizer (WSO) algorithm is applied to solve the model. …”
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Optimizing space heating efficiency in sustainable building design a multi criteria decision making approach with model predictive control by Zheng Qi, Nan Zhou, Xianwei Feng, Sama Abdolhosseinzadeh

“…The research question explores how advanced control strategies can balance heating costs and thermal comfort efficiently. A novel Model Predictive Control (MPC) framework integrates Long Short-Term Memory (LSTM) neural networks for energy demand prediction and the Ant Nesting Algorithm (ANA) for multi-objective optimization. …”
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Shape Optimization of Multi-chamber Acoustical Plenums Using the BEM, Neural Networks, and the GA Method by Ying-Chun CHANG, Ho-Chih CHENG, Min-Chie CHIU, Yuan-Hung CHIEN

“…The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.…”
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EDECO: An Enhanced Educational Competition Optimizer for Numerical Optimization Problems by Wenkai Tang, Shangqing Shi, Zengtong Lu, Mengying Lin, Hao Cheng

“…On the one hand, the estimation of distribution algorithm enhances the global exploration ability and improves the population quality by establishing a probabilistic model based on the dominant individuals provided by EDECO, which solves the problem that the algorithm is unable to search the neighborhood of the optimal solution. …”
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Simplifying the calibration of ecological models by using the parameter estimation tool (PEST): The Curonian Lagoon case by Burak Kaynaroglu, Mindaugas Zilius, Rasa Idzelytė, Artūras Razinkovas-Baziukas, Georg Umgiesser

“…However, subjective and time-consuming manual (trial-and-error) calibration methods cannot ensure optimal parameter match.To address this, we automated the calibration of a newly developed ecological model to improve the simulation of nutrient dynamics as ammonia, nitrate, and phosphate in the estuarine system (Curonian Lagoon). …”
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Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations by Saverio Ieva, Ivano Bilenchi, Filippo Gramegna, Agnese Pinto, Floriano Scioscia, Michele Ruta, Giuseppe Loseto

“…However, existing approaches often treat these aspects in isolation, missing opportunities for optimization and operational efficiency gains through improved information visibility across different roles in the logistics workforce. …”
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Dynamic Reactive Power Optimization Strategy for AC/DC Hybrid Power Grid Considering Different Wind Power Penetration Levels by Nan Feng, Yuyao Feng, Yun Su, Yajun Zhang, Tao Niu

“…Considering the nonlinearity and non-convexity of the optimization model, trajectory sensitivity method and whale optimization algorithm are adopted to enhance the solution efficiency. …”
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Hybrid-driven modeling using a BiLSTM–AdaBoost algorithm for diameter prediction in the constant diameter stage of Czochralski silicon single crystals by Yu-Yu Liu, Ding Liu, Shi-Hai Wu, Yi-Ming Jing

“…In this paper, a hybrid-driven modeling method integrating Bidirectional Long Short-Term Memory network (BiLSTM) and Adaptive Boosting (AdaBoost) algorithm is proposed, aiming to improve the accuracy and stability of crystal diameter prediction in the medium diameter stage of the SSC growth by the Czochralski (CZ) method. …”
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Thermal errors in high-speed motorized spindle: An experimental study and INFO-GRU modeling predictions by Zhaolong Li, Kai Zhao, Haonan Sun, Yongqiang Wang, Bangxv Wang, JunMing Du, Haocheng Zhang

“…The novelty of this study lies in two improvements: firstly, the number of temperature measurement points is optimized by combining a clustering algorithm with a correlation coefficient method, reducing the amount of calculation and the risk of data coupling in the prediction; secondly, the GRU model optimized by the INFO algorithm is applied to the field of electric spindles for the first time, effectively analyzing the dynamic relationship between temperature and thermal expansion. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…This model integrates pre-processing techniques and employs the Tuna Swarm Optimization (TSO) Algorithm for feature selection in executing multi-label disease prediction. …”
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Analysis of Customer Power Consumption Behavior Based on DPSO-Kmeans under the Ubiquitous Power Internet of Things by WANG Ying, XIANG Wen, ZHANG Qun, GAO Xiuyun

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Radial Basis Function Coupling with Metaheuristic Algorithms for Estimating the Compressive Strength and Slump of High-Performance Concrete by Amir Reza Taghavi Khangah, Erfan Khajavi, Hasti Azizi, Amir Reza Alizade Novin

“…The following study represents an important step toward developing novel hybrid models for predicting CS and SL. The contribution in this paper proposes the following: the radial basis function (RBF) model will be enhanced by using two optimization algorithms, namely Horse Herd Optimization (HHO) and Wild Geese Algorithm (WGA). …”
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Research on IoT network performance prediction model of power grid warehouse based on nonlinear GA-BP neural network by Pang Lele, Xia Bo, Cheng Zhanfeng, Ren Zhiqiang, Shen Hao, Li Pengfei

“…This study introduces a novel approach for forecasting network performance prediction in power grid warehouses, employing a nonlinear Genetic Algorithm (GA)-optimized backpropagation (BP) neural network model. …”
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Correlation learning based multi-task model and its application by XU Wei, LUO Jianping, LI Xia, CAO Wenming

“…The experimental results verify the effectiveness of the proposed multi-task learning model based on the correlation layer. Meanwhile, the proposed multi-task learning network as a proxy model is applied to the Bayesian optimization algorithm, which not only reduces the evaluation times of model to target problem, but also enlarges the number of training data exponentially and further improves the model accuracy.…”
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Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction by Liana Toderean, Mihai Daian, Tudor Cioara, Ionut Anghel, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Elissaios Sarmas

“…However, the prediction accuracy of federated learning models tends to diminish when dealing with non-IID data highlighting the need for adaptive hyperparameter optimization strategies to improve performance. …”
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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…This study introduces four explainable Automated Machine Learning (AutoML) models that integrate Optuna for hyperparameter optimization, SHapley Additive exPlanations (SHAP) for interpretability, and ensemble learning algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), and Categorical Gradient Boosting (CB). …”
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Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algori... by Mohamed Bechir Ben Hamida, Ali Basem, Neeraj Varshney, Loghman Mostafa

“…Abstract Optimizing nanofluid thermophysical properties (TPPs) is essential for advancing heat transfer applications; however, most studies focus on two-objective optimization, limiting their real-world applicability. …”
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Model for selective vehicle problem considering mixed fleet with capacitated electric vehicles by Jiacheng Li, Masato Noto, Yang Zhang, Jia Guo

“…Key problems for delivery service providers include how to effectively reduce energy consumption during delivery and improve the daily delivery completion rate. This paper considers the self-loading constraints and energy consumption constraints of different types of trucks and establishes a multi-objective optimization model aimed at maximizing service completion, minimizing service energy consumption, and minimizing emission. …”
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An Enhanced Measurement of Epicardial Fat Segmentation and Severity Classification using Modified U-Net and FOA-guided XGBoost by Rajalakshmi K, Palanivel Rajan S

“…The proposed method integrates a modified squeeze-and-excitation (MSE) block and a multi-scale dense (MS-D) convolutional neural network (CNN) to improve feature extraction. In addition, a metaheuristic optimization algorithm from falcon optimization algorithm (FOA) is used for efficient feature selection. …”
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A Dual-Strategy Framework for Cyber Threat Detection in Imbalanced, High-Dimensional Data Across Heterogeneous Networks by T. Saranya, S. Indra Priyadharshini

“…Second, the Cauchy-Gaussian Genetic-Arithmetic Optimizer (CG-GAO) addresses the challenge of high-dimensional data by combining a genetic algorithm (GA) and an arithmetic optimization algorithm (AOA), enhancing exploration and preventing premature convergence. …”
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