Development of a cognitive blood glucose–insulin control strategy design for a nonlinear diabetic patient model by Dagher Khulood Eskander, Haggege Joseph

“…Additionally, the comparison results demonstrated that the suggested cognitive glucose–insulin control algorithm improved the time to reach a normal physiological blood glucose level by 10% compared to the fuzzy logic and the fractional-order proportional integral derivative (PID) control algorithms, by 25% compared to the type-2 fuzzy control algorithm, and by 6% compared to the PID-particle swarm optimization control algorithm.…”
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Migration and mutation (MeTa) hybrid trained ANN for dynamic spectrum access in wireless body area network by Geoffrey Eappen, Shankar T, Rajesh A

“…Real-time outcomes based on the Universal Software Radio Peripheral (USRP) further validate the efficiency of the proposed method. Compared to particle swarm optimization (PSO)-ANN, classical ANN, and energy detection-based spectrum sensing techniques, the MeTa NN shows detection probability improvements of 9%, 16%, and 37% respectively.…”
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A Novel Deep Learner for Human Behavior Prediction Over Public Video Surveillance by Bayan Alabdullah, Bisma Batool Fatima, Haifa F. Alhasson, Mohammed Alshehri, Yahya AlQahtani, Nouf Albhassabi, Hui Liu

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Wind Turbine Stall‐Induced Aeroelastic Instability Mitigation Using Vortex Generators by Qingshen Meng, Chao Chen, Xugang Hua, Wei Yu

“…Significant improvements are achieved by strategically installing Vortex Generators in the outboard sections of turbine blades and optimizing their parameters (normalized height, length, inflow angle, and chordwise positions) using the particle swarm optimization algorithm and fast optimization method. …”
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A new approach for improving dynamic fault ride through capability of gridctied based wind turbines by Shoaib Ahmed Dayo, Ahsanullah Memon, Zeeshan Anjum Memon, Touqeer Ahmed Jumani, Ghulam Abbas, Salwa Othmen, Amr Yousef, Andika Aji Wijaya

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Adaptive Hybrid PSO-Embedded GA for neuroevolutionary training of multilayer perceptron controllers in VSC-based islanded microgrids by Yared Bekele Beyene, Getachew Biru Worku, Lina Bertling Tjernberg

“…This paper introduces a novel hybrid optimization algorithm, Adaptive Hybrid PSO-Embedded GA (AHPEGA), which dynamically adapts to optimization performance by integrating Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). …”
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Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review by Álex Paz, Broderick Crawford, Eric Monfroy, José Barrera-García, Álvaro Peña Fritz, Ricardo Soto, Felipe Cisternas-Caneo, Andrés Yáñez

“…It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. …”
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Analysis of Techno–Economic and Social Impacts of Electric Vehicle Charging Ecosystem in the Distribution Network Integrated with Solar DG and DSTATCOM by Ramesh Bonela, Sriparna Roy Ghatak, Sarat Chandra Swain, Fernando Lopes, Sharmistha Nandi, Surajit Sannigrahi, Parimal Acharjee

“…In this work, a comprehensive planning framework for an electric vehicle charging ecosystem (EVCE) is developed, incorporating solar distributed generation (DG) and a distribution static compensator (DSTATCOM), to assess their long-term techno–economic and environmental impacts. The optimal locations and capacities of the EVCE, solar DG, and DSTATCOM are determined using an improved particle swarm optimization algorithm based on the success rate technique. …”
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MetaForecaster: A PSO-Driven Neural Model for Sustainable Industrial Air Quality Management by Marzia Ahmed, Shahrin Islam, Mohd Herwan Sulaiman, Md Maruf Hassan, Touhid Bhuiyan

“…To address these challenges, this study proposes an optimized neural forecasting framework integrating Particle Swarm Optimization (PSO) with neural networks. …”
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Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry by Chaowei Dong, Jianfei Xu, Nan Zhou, Jixiong Zhang, Hao Yan, Zejun Li, Yuzhe Zhang

“…Analysis demonstrates that the particle swarm optimal (PSO) algorithm based on adaptive adjustment strategy can effectively optimize the hyperparameters of support vector regression (SVR), and the MC-PSO-SVR model exhibits better predictive capability (R2> 0.88) and lower error coefficients (MAE, RSE, and RMSE values approaching 0) and narrower widths of 95 % confidence intervals for yield stress, plastic viscosity, fluidity, and UCS. …”
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Carbon emission prediction method for expressway construction period based on PSO-BP neural network by Quansheng ZHAO, Fei LI, Feng′ai GUO, Jianyou YU, Shizhao XU, Yunpeng HU, Xiaomeng CHU

“…To solve the problem of inaccurate carbon emissions prediction during the highway construction period, a method of optimizing the back propagation(BP) neural network by particle swarm optimization (PSO) algorithm was proposed to predict carbon emissions. …”
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Enhancing Hajj and Umrah Services Through Predictive Social Media Classification by Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna, Faisal Jamil, Mehdhar S. A. M. Al-Gaashani, Soha Alhelaly, Ahmed Aziz, Ammar Muthanna

“…To improve the effectiveness of this classification model, we introduce a predictive optimization strategy that employs a deep neural network as the learning module and utilizes particle swarm optimization to refine the weighting parameters. …”
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Protection scheme of flexible MTDC transmission line based on ISSA-BiLSTM by LI Zheng, CHEN Tangxian, ZHANG Yunning, LIU Shuangyang, SUN Peisheng

“…Based on wavelet transform technology, the characteristics of transmission line faults are extracted as model input to train the model; the original sparrow search algorithm is improved by using Sine chaotic mapping, learning particle swarm algorithm strategy, and introducing Gaussian disturbance term. …”
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Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging by Rahul Kumar, Cheng-Tang Pan, Yi-Min Lin, Shiue Yow-Ling, Ting-Sheng Chung, Uyanahewa Gamage Shashini Janesha

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A Day-Ahead Economic Dispatch Method for Renewable Energy Systems Considering Flexibility Supply and Demand Balancing Capabilities by Zheng Yang, Wei Xiong, Pengyu Wang, Nuoqing Shen, Siyang Liao

“…This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. …”
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Early Remaining Useful Life Prediction for Lithium-Ion Batteries Using a Gaussian Process Regression Model Based on Degradation Pattern Recognition by Linlin Fu, Bo Jiang, Jiangong Zhu, Xuezhe Wei, Haifeng Dai

“…The model hyperparameters are further optimized through the particle swarm optimization (PSO) algorithm to improve the adaptability and generalization capability of the predictive models. …”
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Estimation and Reduction of CO₂ Emissions From Fossil Fuel Power Plants in Bangladesh by Deepak Kumar Chowdhury, Nur Mohammad, Tanjim Mahmud, Md. Khaliluzzaman, Karl Andersson, Mohammad Shahadat Hossain

“…The paper presents Matpower Interior Point Solver (MIPS), and Dynamic Non-Linear Particle Swarm Optimization (DNPSO) algorithms to solve combined economic emission dispatch (CEED) to optimize generation dispatch and minimize emissions. …”
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Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data by Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi

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Downhole Pressure Pulse Signal Recognition Based on SSA-CNN-LSTM by JIANG Panqin, LIU Xingbin, JIANG Zhicheng, LI Shanwen, HE Zhuang

“…It is found that the SSA-CNN-LSTM algorithm model outperforms traditional LSTM, CNN-LSTM, and PSO (particle swarm optimization) -CNN-LSTM models in terms of both fitting ability and prediction accuracy. …”
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A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model by Ying Xu, Xinrong Ji, Zhengyang Zhu

“…Experimental results demonstrate that the proposed model significantly outperforms standalone benchmark methods. In comparison with Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and the equal-weight assignment approach for high- and low-frequency component forecasting, the proposed SO algorithm attains the lowest forecasting errors. …”
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