Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete by Mana Alyami, Muhammad Faisal Javed, Irfan Ullah, Hisham Alabduljabbar, Furqan Ahmad

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Secrecy-Constrained UAV-Mounted RIS-Assisted ISAC Networks: Position Optimization and Power Beamforming by Weichao Yang, Yajing Wang, Dawei Wang, Yixin He, Li Li

“…To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. …”
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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…The use of CBO ensures efficient convergence with minimal parameter tuning, making the model suitable for large-scale datasets compared to conventional optimizers, including Adam, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). …”
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Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo by Ardiansyah, Sri Handayaningsih, Deva Fathurrizki

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Model order reduction of boiler system using nature-inspired metaheuristic optimization of PID controller by Anurag Singh, Shekhar Yadav, Nitesh Tiwari, Dinesh Kumar Nishad, Saifullah Khalid

“…The PID controllers are optimized using both classical methods such as Ziegler-Nichols (ZN), Simple Internal Model Control (SIMC), Approximate M-Constrained Integral Gain Optimization (AMIGO), and Chien-Hrones-Reswick (CHR), as well as advanced optimization techniques like Particle Swarm Optimization (PSO), Krill Herd Optimization (KHO), Harris Hawks Optimization (HHO), Moth-Flame Optimization (MFO), and Sparrow Search Optimization (SSO). …”
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Optimization of rotor-side controller parameters in doubly fed induction generators based on an improved NSGA-II. by Yanling Lv, Xiang Zhao, Zexin Mou

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”
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Structural optimization design and compression performance of porous titanium prepared by laser powder bed fusion by Yabao Hu, Hongchuan Li, Hanning Chen, Songpeng Zhang, Zhixue Wang, Jianbo Lei

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Damage Identification in Bars with a Wave Propagation Approach: Performance Comparison of Five Hybrid Optimization Methods by R.A. Tenenbaum, L.T. Stutz, K.M. Fernandes

“…The hybridization is performed combining the deterministic Levenberg-Marquardt method and each one of the following stochastic techniques: The Particle Swarm Optimization; the Luus-Jaakola optimization method; the Simulated Annealing method; the Particle Collision method; and a Genetic Algorithm. …”
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Uncertainty aware energy management in microgrids with integrated electric bicycle charging stations and green certificate market by Hossein Shayeghi, Iraj Faraji Davoudkhani

“…MTBO is benchmarked against particle swarm optimization, achieving a 21.6% reduction in operational costs and a 13.12% reduction in emissions in deterministic scenarios. …”
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Multiobjective Optimization of Large-Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station by Weicheng Hou, Qingsong Luo, Xiangdong Wu, Yimin Zhou, Gangquan Si

“…Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. …”
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Identification of significant bio-markers from high-dimensional cancerous data employing a modified multi-objective meta-heuristic algorithm by Prajna Paramita Debata, Puspanjali Mohapatra

“…Here, the suggested algorithm has been compared with multi-objective chaotic Genetic Algorithm (MOCGA), multi-objective chaotic particle swarm optimization (MOCPSO), multi-objective Jaya (MOJaya), multi-objective PSO (MOPSO), and non-dominated sorting GA (NSGA-II) models. …”
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Enhancing Load frequency control in power systems using Puma Optimizer – Proportional Integral Derivative Method by Trung Kien Do, Thanh Long Duong

“…The simulation results show that the integral time absolute error (ITAE) value of the proposed PO method is smaller by 5.25%, 18.16%, 28.35%, and 59.92% compared to Particle Swarm Optimization (PSO), Crested Porcupine Optimization (CPO), Newton-Raphson-based optimization (NRBO), and Global Neighborhood Algorithm (GNA), respectively. …”
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Optimizing Joint Decisions of Dynamic Pricing and Ticket Allocation for High-Speed Railway with Operators’ Risk Preference by Huaqin Meng, Zhenying Yan, Yu Wang, Yiwei Xu

“…Then, the combination algorithm of particle swarm optimization algorithm and mixed-integer linear programming was designed to solve the model. …”
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Linear Active Disturbance Rejection Control System for the Travel Speed of an Electric Reel Sprinkling Irrigation Machine by Lingdi Tang, Wei Wang, Chenjun Zhang, Zanya Wang, Zeyu Ge, Shouqi Yuan

“…To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. …”
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Power Frequency Oscillation Suppression Using Two-Stage Optimized Fuzzy Logic Controller for Multigeneration System by Y. K. Bhateshvar, H. D. Mathur

“…A comparison between genetic algorithm optimized PID controller (GA-PID), particle swarm optimized PID controller (PSO-PID), and proposed two-stage based PSO optimized fuzzy logic controller (TSO-FLC) is presented. …”
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RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture by Zongnan Zhang, Jun Du, Menghan Li, Jing Guo, Zhenyang Xu, Weikang Li

“…Moreover, the upper model is solved by transforming the model into a mixed-integer linear programming problem and calling CPLEX, and the lower model is a multi-objective planning problem, which is solved by improving the small-habitat particle swarm algorithm. Finally, the simulation is validated by the MATLAB platform, and the results show that the simultaneous consideration of carbon capture system and P2G facility improves the economics of the integrated energy system and the capacity of wind and solar energy accommodation.…”
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Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm by Roozbeh Moazenzadeh, Okan Mert Katipoğlu, Ahmadreza Shateri, Hamid Nasiri, Mohammed Abdallah

“…The superiority of the proposed model (XGB-MPA) compared to two other hybrid models, including XGB-PSO (Particle Swarm Optimization) and XGB-GWO (Grey Wolf Optimization) was also investigated. …”
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Optimal design and operation of a wind farm/battery energy storage considering demand side management by Siyu Tao, Chaohai Zhang, Andrés E. Feijóo‐Lorenzo, Victor Kim

“…It is tackled by the mixed‐discrete particle swarm optimization algorithm. The multi‐objective optimization problem (MOOP) model in the second level determines the operation schedule of the WF/BES and other generators taking the DSM into consideration. …”
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Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method by Teng Jing, Yu Yang, Weimin Ru

“…The finite element method (FEM) was employed to optimize the physical parameters of the system. Based on this optimization, two intelligent PID tuning strategies—particle swarm optimization (PSO) and backpropagation (BP) neural networks—were compared. …”
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Hybrid Optimization based on Deep Learning Approach for Short-Term Load Forecast of Electricity Demand in Buildings by Charan Sekhar Makula, Ratna Dahiya

“…The performance of the BiLSTM technique is improved by using the optimally determined hyperparameters via a hybrid optimization algorithm that combines particle swarm optimization (PSO) and grey wolf optimization (GWO). …”
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