Optimum design of double tuned mass dampers using multiple metaheuristic multi-objective optimization algorithms under seismic excitation by Fateme Zamani, Sayyed Hadi Alavi, Mohammadreza Mashayekhi, Ehsan Noroozinejad Farsangi, Ataallah Sadeghi-Movahhed, Ali Majdi

“…However, implementing multiple-tuned mass dampers can also improve seismic performance while reducing the required mass. …”
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A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion by Dongfang Mao, Guoping Jiang, Yun Zhao

“…This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). …”
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A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters by Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu

“…This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH). …”
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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

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Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis by Adama Ndour, Gerald Blasch, João Valente, Bisrat Haile Gebrekidan, Tesfaye Shiferaw Sida

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A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations by Intan Berlianty, Irwan Soejanto, Indun Titisariwati, Favian Ersanta Andhika Putra, Wahyu Tri Utami, Mohamad Kamil Insani

“…Fuzzy Logic processes this data, while GA optimizes the system's parameters for enhanced accuracy and adaptability. …”
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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…In this paper, we propose a general framework that combines advanced deep learning models (such as GRU, Bidirectional GRU (BIGRU), Stacked GRU, and Attention-based BIGRU) with a novel hybridized optimization algorithm, GGBERO, which is a combination of Greylag Goose Optimization (GGO) and Al-Biruni Earth Radius (BER). …”
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Passenger Flow Prediction for Rail Transit Stations Based on an Improved SSA-LSTM Model by Xing Zhao, Chenxi Li, Xueting Zou, Xiwang Du, Ahmed Ismail

“…According to the experimental results, the proposed SSA-LSTM model has a more effective performance than the Support Vector Regression (SVR) method, the eXtreme Gradient Boosting (XGBoost) model, the traditional LSTM model, and the improved LSTM model with the Whale Optimization Algorithm (WOA-LSTM) in the passenger flow prediction. …”
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Optimization of a Navigation System for Autonomous Charging of Intelligent Vehicles Based on the Bidirectional A* Algorithm and YOLOv11n Model by Shengkun Liao, Lei Zhang, Yunli He, Junhui Zhang, Jinxu Sun

“…Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. …”
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A New Method for Spectral Wavelength Selection Based on Multiple Linear Regression Combined with Ant Colony Optimization and Genetic Algorithm by Qing Huang, Heru Xue, Jiangping Liu, Xinhua Jiang

“…Wavelength selection is one of the key steps in quantitative spectral analysis, which reduces the computation time while also improving the prediction accuracy of the model. …”
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Mexican axolotl optimization algorithm with a recalling enhanced recurrent neural network for modular multilevel inverter fed photovoltaic system by R. Madavan, B. Karthikeyan, R. Palanisamy, Mohammad Imtiyaz Gulbarga, Mohammed Al Awadh, Liew Tze Hui

“…The proposed MAO-RERNN control method integrates the Mexican Axolotl Optimization (MAO) algorithm with a Recalling-Enhanced Recurrent Neural Network (RERNN) to achieve optimal power conversion, improved stability, and reduced total harmonic distortion (THD). …”
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Optimal Distributed Dispatch of Smart Multi-Agent Energy Hubs Based on Consensus Algorithm Considering Lossy Communication Network and Uncertainty by Abdelfattah A. Eladl, Magda I. El-Afifi, Magdi M. El-Saadawi, Bishoy E. Sedhom

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Optimizing Autonomous Multi-UAV Path Planning for Inspection Missions: A Comparative Study of Genetic and Stochastic Hill Climbing Algorithms by Faten Aljalaud, Yousef Alohali

“…GA exemplifies the global search strategy, while HC illustrates an enhanced stochastic local search. …”
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Cost-Based Optimal Allocation of Shunt Capacitors in Radial Distribution Networks Considering Load Types Using Crow Search Algorithm by Stephen W. Mathenge, Edwell. T. Mharakurwa, Lucas Mogaka

“…CSA’s performance was benchmarked against invasive weed optimization (IWO), teacher learner-based optimization (TLBO), and artificial bee colony (ABC) algorithms. …”
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High-efficiency Axial Flow Fan Design by Combining Through-flow Modeling, Optimization Algorithm and Computational Fluid Dynamics Simulation by C. Lee, S. W. Kim, H. T. Byun, S. H. Yang

“…The optimization algorithm is applied to the fan design and through-flow analysis program, achieving a very fast and simple optimization process and obtaining the optimal axial fan model. …”
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Design of a liquid cooled battery thermal management system using neural networks, cheetah optimizer and salp swarm algorithm by Anjan Kumar, Laith Hussein Jasim, Padmanabha Vijaya, Dipak Patel, J. Gowrishankar, R. Sivaranjani, Ankur Srivastava, Mayank Kundlas, Sarbeswara Hota, Banafshe Hamidi

“…In the first phase, predictive modeling was performed using multilayer perceptron neural networks (MLPNN) optimized by three metaheuristic algorithms: cheetah optimizer (CO), grey wolf optimizer (GWO), and marine predators algorithm (MPA). …”
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Parameter extraction of PV models under varying meteorological conditions using a modified electric eel foraging optimization algorithm by Hadeer Khalifa, Mohamed Ebeed, Gaber Magdy, Sherif A. Khaleel, Mohamed I. Shehata, Moataz M. Salah, Francisco Jurado, Hossam Hassan Ali

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Remaining useful life prediction of a small sample of aero-engine based on an improved gray Markov model by Dong-hai Li, Ji-liang Tu, Hui Liu, Nai-zhi Liao

“…To enhance adaptability, a genetic algorithm dynamically optimizes the time response parameters of the grey model, overcoming the limitation of a fixed structure in traditional methods. …”
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Structural Parameter Identification Using Multi-Objective Modified Directional Bat Algorithm by LIU Li-jun, LIN Ying-hai, SU Yong-hui, LEI Ying

“…This approach improved the accuracy and robustness of structural parameter identification while maintaining computational efficiency.MethodsMOMDBA is an enhanced version of the Directional Bat Algorithm （DBA）， a swarm intelligence optimization technique inspired by the echolocation behavior of bats. …”
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Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system by Xinhui LI, Qing SHEN, Xiongtao ZHANG

“…A new classification model of electrocardiogram (ECG) signal based on particle swarm optimization feature selection (PSOFS) and TSK (Takagi-Sugeno-Kang) fuzzy system was proposed, i.e., parallel ensemble fuzzy neural network based on PSOFS and TSK (PE-PT-FN), which was used for ECG prediction.Each class sample in the training set was randomly sampled, and the samples obtained by randomly sampled were added.Then, the feature selection method PSOFS was carried out independently and parallelly.In PSOFS, particles that were random initial positions represent different feature subsets and converge to the optimal positions after many iterations.Each subset had a corresponding feature subset.Several groups of TSK fuzzy neural network (TSK-FNN) were trained by each feature subset in parallel.Medical researchers could effectively find the correlation between ECG signal data and different types of disease through the interpretability of the fuzzy system and the feature subsets by the PSOFS algorithm.Experiments prove that PE-PT-FN greatly improves the macro-R to 92.35% while retaining interpretability.…”
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