An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering by Laith Abualigah, Saleh Ali Alomari, Mohammad H. Almomani, Raed Abu Zitar, Hazem Migdady, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Absalom E. Ezugwu

“…The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. …”
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Multi-Condition Magnetic Core Loss Prediction and Magnetic Component Performance Optimization Based on Improved Deep Forest by Haotian Shi, Zipeng Jin

“…To achieve high efficiency and high power density designs, it is essential to study core loss characteristics and optimize operating parameters. This paper proposes a core loss prediction model based on an improved deep forest algorithm and information entropy-enhanced genetic algorithm. …”
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CSO Intelligent Optimization of Drag Torque Parameters of Wet Brakes Based on the SSA-BP Approximate Model by Li Jie, Wang Shuai, Lan Hai, Wang Zhiyong

“…Compared with the traditional BP model, the prediction accuracy is obviously improved, which can meet the needs of practical engineering. …”
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Hidden-layer configurations in reinforcement learning models for stock portfolio optimization by Patrick Kevin Aritonang, Sudarso Kaderi Wiryono, Taufik Faturohman

“…This study explores the impact of hidden-layer configurations in reinforcement learning models for stock portfolio optimization. Using a portfolio of 45 actively traded stocks in the Indonesian stock market, the performance of four reinforcement learning algorithms—Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Twin Delayed Deep Deterministic Policy Gradient (TD3)—is evaluated with zero, one, and two hidden layers. …”
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Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes by Xiaomei Zhang, Ziang Liu, Jialiang Zhang, Yuhang Zeng, Chuannian Fan

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Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters by Vu Van Tan, Do Trong Tu, Nguyen Van Vinh, Pham Tat Thang, Andras Mihaly, Peter Gaspar

“…Subsequently, the coefficients of the HASS are optimized through the Genetic Algorithm optimization with the parameter <inline-formula> <tex-math notation="LaTeX">$\beta =0.1,\,0.5,\,0.9$ </tex-math></inline-formula>, aiming to enhance ride comfort and road holding corresponding to each value of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. …”
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Building Energy Optimization Using an Improved Exponential Distribution Optimizer Based on Golden Sine Strategy Minimizing Energy Consumption Under Uncertainty by Mohammad Ali Karbasforoushha, Mohammad Khajehzadeh, Suraparb Keawsawasvong, Lapyote Prasittisopin, Thira Jearsiripongkul

“…In this study, a new improved meta-heuristic algorithm is proposed for solving the energy building optimization (EBO) and also hybrid energy systems optimization considering uncertainty of conditioned surface area subjected to temperature control for BEO and renewable power and load uncertainties for hybrid system. …”
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A Bi-Objective Model for the Location and Optimization Configuration of Kitchen Waste Transfer Stations by Ming Wan, Ting Qu, George Q. Huang, Ruoheng Chen, Manna Huang, Yanghua Pan, Duxian Nie, Junrong Chen

“…An improved non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) is used to solve the problem, resulting in a Pareto-optimal solution set that includes several non-dominated solutions, thereby providing diversified choices for decision-makers. …”
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Research on Control Method of Electron Accelerator Based on Simulink Simulation by YUE Hongwei1, LI Zhongping2, 3, ZHOU Youwei2, CAO Shuchun2, 3, REN Jieru1, ZHANG Zimin2, 3, , ZHAO Yongtao1

“…The fuzzy neural network PID algorithm improves these indicators more significantly, the stabilization time is reduced by 77.9%, the overshoot is reduced by 79.6%, and the post-disturbance recovery time is reduced by 87.1%. …”

Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods by Wenchao Li, Houmin Li, Cai Liu, Kai Min

“…Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. …”
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An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain by Muhammad Asrol, . Suharjito, Riyanto Jayadi

“…This research contributes to improving the model by employing genetic algorithms in optimizing the inference model by activating only five out of seven rules. …”
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Model Predictive Control of Nonlinear System Based on GA-RBP Neural Network and Improved Gradient Descent Method by Youming Wang, Didi Qing

“…A model predictive control (MPC) method based on recursive backpropagation (RBP) neural network and genetic algorithm (GA) is proposed for a class of nonlinear systems with time delays and uncertainties. …”
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Joint Optimization Method for Preventive Maintenance and Train Scheduling of Subway Vehicles Based on a Spatiotemporal Network Graph by Chuanzhen Liu, Zhongwei Xu, Meng Mei, Wenqing Li

“…Finally, an improved genetic algorithm is employed to solve the model and determine the optimal scheduling and maintenance strategy. …”
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Optimization of power output in plateau photovoltaic power stations using a hybrid Kepler and Gaussian quantum particle swarm algorithm by Shuang Gan, Heng Hu, Shaoshuai Li, Qian Peng, Taidong Yan, Huasheng Gong, Yuancheng Zhang

“…The proposed solution integrates the Kepler Optimization Algorithm (KOA) with the Gaussian Quantum Improved Particle Swarm Optimization (GQPSO) to address multi-objective optimization, with the goal of maximizing power generation, minimizing operational costs, and enhancing system stability. …”
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Optimizing Photovoltaic Grid‐Connected Power Systems Through Artificial Intelligence and Robust Nonlinear Control by Collins Ngong Sang, Harrison Ambe, Arnaud flanclair Tchouani Njomo, Zeric Tabekoueng Njitacke, Wulfran Fendzi Mbasso, Pradeep Jangir, Aseel Smerat

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Improved artificial protozoa optimizer: A new method for solar photovoltaic parameter estimation by Wenhao Lai, Duoduo Liu, Jialong Yang, Lei Guo, Weijin Qian, Jiaojiao Wu, Haifeng Zhou

“…The accuracy of parameters in solar cell models is helpful for optimizing the maximum power point tracking, which allows for an improvement in power generation efficiency. …”
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Electrical Fault Diagnosis Method for Tunneling Equipment Based on Fault Simulation and Optimized Particle Swarm Algorithm by Huan Zou, Xueping Zhang, Xin Wang, Yu Li

“…To address the challenges of scarce training data and poor adaptability to dynamic working conditions in electrical fault diagnosis for tunneling equipment, this study proposes an intelligent diagnosis model based on fault simulation and Improved Particle Swarm Optimization (IPSO). …”
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Determining Appropriate Strategy for Managing the Energy Production System in the Presence of Renewable Energy Sources and Improved Intelligence Method by Amir Sohel, Md Faisal Bin Ashab

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Network Security Situational Awareness Based on Improved Particle Swarm Algorithm and Bidirectional Long Short-Term Memory Modeling by Peng Zheng, Yun Cheng, Wei Zhu, Bo Liu, Shuhong Liu, Shijie Wang, Jinyin Bai

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Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO by Enlai ZHANG, Yi CHEN, Liang SU, Ruoyu ZHONGLIAN, Xianyi CHEN, Shangfeng JIANG

“…Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost, and PSO-XGBoost through data training. …”
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