Optimization Study of Centrifugal Fan Volute Parameters based on Non-dominated Sorting Genetic Algorithm III Algorithm by J. L. Li, X. J. Wang, H. Gong, J. J. Wang

“…The BP neural network provided highly accurate fitting and predictions, yielding a reliable surrogate model. After optimization, the centrifugal fan’s Q increased by 2.29%, and η improved by 2.96%. …”
Get full text

Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning by Wajahat Hussain, Muhammad Faheem Mushtaq, Mobeen Shahroz, Urooj Akram, Ehab Seif Ghith, Mehdi Tlija, Tai-hoon Kim, Imran Ashraf

“…The GA optimizes the number of layers, kernel size, learning rates, dropout rates, and batch sizes of the CNN model to improve the accuracy and performance of the model. …”
Get full text

Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach by Yunus Emre Ekici, Teoman Karadağ, Ozan Akdağ, Ahmet Arif Aydin, Hüseyin Ozan Tekin

“…Among these, the Developed Honey Badger Algorithm with AI Approach (DHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). …”
Get full text

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data by Guofu Zhao

“…The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. …”
Get full text

Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
Get full text

Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods by Tayfun Abut, Enver Salkım, Andreas Demosthenous

“…The optimum values of the coefficients of the points where the membership functions of the LQG and Fuzzy LQG methods touch were obtained using the grey wolf optimization (GWO) algorithm. The success of the control performance rate of the applied methods was compared based on the passive suspension system. …”
Get full text

Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations by Saverio Ieva, Ivano Bilenchi, Filippo Gramegna, Agnese Pinto, Floriano Scioscia, Michele Ruta, Giuseppe Loseto

“…However, existing approaches often treat these aspects in isolation, missing opportunities for optimization and operational efficiency gains through improved information visibility across different roles in the logistics workforce. …”
Get full text

A novel extraction model optimization with effective separation coefficient for rare earth extraction process using improve differential evolution by Fangping Xu, Hui Yang, Jianyong Zhu, Wenjia Chang

“…Taking into account the multi-modal and multi-variable characteristics of the optimized objective function, we put forth an enhanced version of the improved differential evolution algorithm, the Linear-Chaos and Two Mutation Strategies of Adaptive Differential Evolution (LCTADE)with Covariance Matrix and Cauchy Perturbation(CC-LCTADE). …”
Get full text

Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm by Mi Zhang, Guosheng Zhou, Bei Liu, Dajun Huang, Hao Yu, Li Mo

“…Validation on classical test functions and the Jiangpinghe River of the multi-timescale nested optimal scheduling model demonstrates that MPWOA exhibits faster convergence and stronger optimization capabilities and significantly improves power generation. …”
Get full text

A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…These findings offer a valuable reference for optimizing PSC process parameters and improving performance, thereby saving time and labor costs.…”
Get full text

Hybrid Models for Forecasting Allocative Localization Error in Wireless Sensor Networks by Guo Li, Hongyu Sheng

“…The approach utilizes Radial Basis Function (RBF) models enhanced with advanced optimization algorithms, including Coot Optimization Algorithm (COA), Smell Agent Optimization (SAO), and Northern Goshawk Optimization (NGO) to improve ALE prediction accuracy. …”
Get full text

Comprehensive Study of Nonlinear Maglev System Utilizing COOT Optimized FOPID Controller by Marabathina Maheedhar, T. Deepa

“…To improve the performance of the magnetic levitation system, the most recent metaheuristic COOT algorithm was first employed in this study to tune the Fractional Order Proportional Integral and Derivative (FOPID) controller. …”
Get full text

Multi-criteria decision model for multicircular flight control of unmanned aerial vehicles through a hybrid approach by Noorulden Basil, Hamzah M. Marhoon, Bayan Mahdi Sabbar, Abdullah Fadhil Mohammed, Osamah Albahri, Ahmed Albahri, Abdullah Alamoodi, Iman Mohamad Sharaf, Amare Merfo Amsal, Mahrous Ahmed, Enas Ali, Sherif S. M. Ghoneim

“…This study provides a robust solution for UAV control based on the potential of hybrid optimization algorithms to improve UAV precision and reliability in autonomous flight.…”
Get full text

A Study on Hysteresis Stiffness Model and Parameter Identification of Harmonic Gear Transmission based on Genetic Characteristic by Linfeng Qiu, Manyi Chen, Gang Song, Jie Zhang, Ran Yang, Han Zhang

“…Based on the experimental data，particle swarm optimization algorithm is used to identify the model parameters. …”
Get full text

Research on early warning model of coal spontaneous combustion based on interpretability by Huimin Zhao, Xu Zhou, Jingjing Han, Yixuan Liu, Zhe Liu, Shishuo Wang

“…The grid search algorithm was utilized to optimize the model parameters, ensuring the selection of the most suitable parameter configurations. …”
Get full text

Innovative Business Models Towards Sustainable Energy Development: Assessing Benefits, Risks, and Optimal Approaches of Blockchain Exploitation in the Energy Transition by Aikaterini Papapostolou, Ioanna Andreoulaki, Filippos Anagnostopoulos, Sokratis Divolis, Harris Niavis, Sokratis Vavilis, Vangelis Marinakis

“…This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. …”
Get full text

A comparative study of hybrid adaptive neuro-fuzzy inference systems to predict the unconfined compressive strength of rocks by Wei Cao

“…However, the inclusion of optimization algorithms significantly improved the performance of the SVR framework. …”
Get full text

New QSPR/QSAR Models for Organic and Inorganic Compounds: Similarity and Dissimilarity by Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati

“…<b>Background:</b> We studied in silico models of both organic and inorganic substances. In most cases, these in silico models are used for organic substances only. …”
Get full text

Task-dependent Optimal Weight Combinations for Static Embeddings by Nathaniel Robinson, Nathaniel Carlson, David Mortensen, Elizabeth Vargas, Thomas Fackrell, Nancy Fulda

Get full text

Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. …”
Get full text