Multi-Parameter Optimization Using Metaheuristic Algorithms to Improve Uncrewed Aerial Vehicles’ Wireless Communications: A Performance Analysis by Lalan J. Mishra, Naima Kaabouch

“…Seven metaheuristic algorithms were applied to both approaches, and the performance was evaluated via convergence and processing times. …”
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Fusion of Visible and Infrared Images Using a Reinforcement Learning System Based on Fuzzy Logic and Convolution Optimized with Wild Horse Algorithm by Mahvash Zarimeidani, Amir Amirabadi, Nasrin Amiri, Iman Ahanian, Siavash Es’haghi

“…This hybrid reinforcement learning system was optimized using algorithms including wild horse optimization (WHO), genetic algorithm (GA), and particle swarm optimization (PSO) to improve specific fusion metrics such as image correlation, similarity coefficient, image entropy, and signal-to-noise ratio. …”
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An Improved Marriage in Honey-Bee Optimization Algorithm for Minimizing Earliness/Tardiness Penalties in Single-Machine Scheduling with a Restrictive Common Due Date by Pedro Palominos, Mauricio Mazo, Guillermo Fuertes, Miguel Alfaro

“…This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. …”
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Optimizing LoRaWAN Gateway Placement in Urban Environments: A Hybrid PSO-DE Algorithm Validated via HTZ Simulations by Kanar Alaa Al-Sammak, Sama Hussein Al-Gburi, Ion Marghescu, Ana-Maria Claudia Drăgulinescu, Cristina Marghescu, Alexandru Martian, Nayef A. M. Alduais, Nawar Alaa Hussein Al-Sammak

“…This study investigates how to optimize the placement of LoRaWAN gateways by using a combination of Particle Swarm Optimization (PSO) and Differential Evolution (DE). …”
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Battle Royale Optimization for Optimal Band Selection in Predicting Soil Nutrients Using Visible and Near-Infrared Reflectance Spectroscopy and PLSR Algorithm by Jagadeeswaran Ramasamy, Anand Raju, Kavitha Krishnasamy Ranganathan, Muthumanickam Dhanaraju, Backiyathu Saliha, Kumaraperumal Ramalingam, Sathishkumar Samiappan

“…In order to select optimum bands (wavelength) from the soil spectra, we have employed metaheuristic algorithms i.e., Particle Swarm Optimization (PSO), Moth–Flame optimization (MFO), Flower Pollination Optimization (FPO), and Battle Royale Optimization (BRO) algorithm. …”
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Sensorless real-time solar irradiance prediction in grid-connected PV systems using PSO-MPPT and IoT-enabled monitoring by Ali Zaki Mohammed Nafa, Adel A. Obed, Ahmed J. Abid, Salam J. Yaqoob, Mohit Bajaj, Mohammad Shabaz

“…The approach leverages the maximum power point current ( $$\:{\text{I}}_{\text{mpp}}$$ ) and voltage ( $$\:{\text{V}}_{\text{mpp}}$$ ) measured directly from a PV module to predict irradiance, utilizing a Particle Swarm Optimization (PSO)-based Maximum Power Point Tracking (MPPT) algorithm to ensure accurate tracking of power output across varying irradiance levels. …”
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A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model by WANG Hongtao, ZHANG Liwei, MU Gang

“…The model approximates the working characteristics of the thermostatically controlled loads (TCL). The particle swarm optimization algorithm is used to optimize the parameters in the model, and the error is corrected by the linear regression equation. …”
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A refined Greylag Goose optimization method for effective IoT service allocation in edge computing systems by Hossein Najafi Khosrowshahi, Hadi S. Aghdasi, Pedram Salehpour

“…This problem remains challenging due to dynamic workloads and heterogeneous resources. Existing swarm intelligence algorithms, such as QPSO-SP and WOA-FSP, often struggle to balance exploration and exploitation effectively. …”
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Speech emotion recognition based on a stacked autoencoders optimized by PSO based grass fibrous root optimization by Chi Zeng, Jialing Li, Abbas Habibi

“…This study introduces an innovative approach that merges deep learning with metaheuristic algorithms to boost the efficiency of SER systems. Specifically, a stacked autoencoder (SAE) serves as the primary model, and its performance is fine-tuned using a nature-inspired hybrid algorithm that combines particle swarm optimization (PSO) with Grass Fibrous Root Optimization (GFRO). …”
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Improving energy efficiency for intelligent reflecting surface assisted PD-NOMA in EH relaying network by Hong Nguyen-Thi, Thuc Kieu-Xuan, Thang Le-Nhat, Anh Le-Thi

“…To determine the efficiency of PSO, we consider two other techniques: Genetic Algorithm (GA) and Exhaustive Search (ES). Finally, we evaluate the proposed model's performance in different scenarios, such as the number of IRS elements and the number of relay nodes, the relaying selection strategies, and the transmit power at the BS. …”
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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…The two sets of selected features are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approach respectively.  …”
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Fuzzy LQR-based control to ensure comfort in HVAC system with two different zones by Elif Çinar, Tayfun Abut

“…The core novelty of this work lies in the development and comparison of advanced control algorithms, including the Linear Quadratic Regulator (LQR), a Particle Swarm Optimization (PSO)-based LQR, and a newly designed PSO-based Fuzzy LQR (FLQR) controller. …”
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Enhancing Wind Turbine Efficiency: An Experimental Investigation of a Sensorless Three-Vector Finite Set Predictive Torque Control Approach for PMSG-Based Systems by Marouane Ahmed Ghodbane, Toufik Mohamed Benchouia, Mohamed Chebaani, Mohamed Becherif, Yassine Himeur, Amar Golea, Abdelmoumen Ghilani, Zakaria Alili, Shadi Atalla, Wathiq Mansoor

“…This paper presents a new control strategy that combines two intelligent estimation techniques—the Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO)—with Three-Vector Finite Set Predictive Torque Control (3V FS-PTC). …”
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Reliability evaluation and multi-objective optimization of combustion chamber’s key components of marine engine by Lei Hu, Wentong Wang, Xu Wang, Jianguo Yang, Yonghua Yu, Chunyang Mei

“…Constrained multi-objective optimization of reliability is conducted through contrastive analysis of different optimization algorithms. The research shows that the multi-objective particle swarm optimization algorithm achieves the best performance, the maximum temperatures of the piston, cylinder head, and liner decrease by 3.90 %, 5.66 %, and 6.52 %, the maximum thermo-mechanical coupling stresses reduced by 9.41 %, 7.83 %, and 4.97 % respectively, and creep-fatigue life enhancements reach 3.84 % and 12.67 % for the piston and cylinder head. …”
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A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression by Tingkai Hou, Zonghong Zhou, Yonggang Zhang, Jing Zhang

“…Secondly, the DE-GWO, particle swarm optimization (PSO), genetic algorithm (GA), and SVR are integrated to identify the optimal superparameters, while the nonlinear mapping relationship between inversion parameters and displacements is established. …”
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Model updating method for detect and localize structural damage using generalized flexibility matrix and improved grey wolf optimizer algorithm (I-GWO) by Sina Sadraei, Majid Gholhaki, Omid Rezaifar

“…Employing optimization algorithms in structural model updating is one approach to achieve this objective. …”
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Optimization of Home Energy Management Systems in Smart Cities Using Bacterial Foraging Algorithm and Deep Reinforcement Learning for Enhanced Renewable Energy Integration by Mohammed Naif Alatawi

“…A robust methodology is established, encompassing data collection from smart homes, implementation details of the BFMO algorithm, DRL techniques, and a comprehensive evaluation framework. …”
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Magnetic Actuation for Wireless Capsule Endoscopy in a Large Workspace Using a Mobile-Coil System by Xiao Li, Detian Zeng, Han Xu, Qi Zhang, Bin Liao

“…In particular, the proportional–integral–derivative (PID) control parameters and current values are optimized online and in real time using the adaptive particle swarm optimization (APSO) algorithm. In this paper, both simulations and real-world experiments were conducted using acrylic plates with irregular shapes to simulate the GI tract environment for evaluation. …”
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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN by TIAN LiYong, ZHAO JianJun, YU Ning

“…Aiming at the high dependence of super parameter selection on artificial experience in rolling bearing state identification based on convolution neural network(CNN),a fault diagnosis model(CNN⁃MA)based on mayfly algorithm(MA)was proposed.Firstly,the model used the powerful optimization ability of MA,took the diagnostic accuracy of CNN as the optimization objective,and adaptively adjusted the super parameters in CNN.Secondly,the normalized original signal image set was used to preserve the characteristics of the signal as much as possible.Finally,in order to evaluate the effectiveness of the parameters in the optimization model,compared with the CNN model optimized by particle swarm optimization(PSO)algorithm.The results show that the proposed model has more stable performance,higher recognition accuracy and good anti⁃noise ability.It fully shows the feasibility and reliability of CNN⁃MA model in fault diagnosis of rolling bearings.…”
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