SSM-Net: Enhancing Compressed Sensing Image Reconstruction with Mamba Architecture and Fast Iterative Shrinking Threshold Algorithm Optimization by Xianwei Gao, Bi Chen, Xiang Yao, Ye Yuan

Get full text

Application of the YOLOv11-seg algorithm for AI-based landslide detection and recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

Get full text

Puma algorithm for environmental emissions and generation costs minimization dispatch in power systems by Badr Al Faiya, Ghareeb Moustafa, Hashim Alnami, Ahmed R. Ginidi, Abdullah M. Shaheen

“…It efficiently navigates the solution space by balancing exploration and exploitation, leveraging puma-like intelligence to minimize both fuel costs and greenhouse gas emissions, including CO2, NOx, and SO2. The POO algorithm is tested on the IEEE 30-bus power system with six thermal units, delivering superior performance compared to advanced optimization algorithms such as the Osprey Optimization Algorithm (OOA), Aquila Optimizer (AO), Slim Mould Algorithm (SMA), Artificial Rabbit Optimization (ARO), and Coati optimization technique. …”
Get full text

PERFORMANCE OPTIMIZATION OF TRANSMISSION GEAR BASED ON OPTIMAL MODIFICATION DESIGN by HAN Wei, REN ZhiQun

“…At the same time,it can be proved that this method can effectively improve the meshing condition of gears and is an effective means to optimize the meshing performance of gears.…”
Get full text

Algorithms for big data mining of hub patent transactions based on decision trees by Zhukov Aleksandr, Pronichkin Sergey, Mihaylov Yuri, Kartsan Igor

“…Based on evolutionary computing, the optimal values of the parameters of algorithms for big data mining of hub patent transactions have been established.…”
Get full text

Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm by Manish Kumar Singla, Jyoti Gupta, Ramesh Kumar, Pradeep Jangir, Mohamed Louzazni, Nimay Chandra Giri, Ahmed Jamal Abdullah Al-Gburi, E. I.-Sayed M. EI-Kenawy, Amal H. Alharbi

“…This research introduces a novel approach for optimal SOFC model identification using a differential evolutionary mutation Fennec fox algorithm (DEMFFA). …”
Get full text

Optimal Design of Multiband Microstrip Antennas by Self-Renewing Fitness Estimation of Particle Swarm Optimization Algorithm by Xiaohong Fan, Yubo Tian, Yi Zhao

“…In order to reduce the time of designing microstrip antenna, this paper proposes a self-renewing fitness estimation of particle swarm optimization algorithm (SFEPSO) to improve the design efficiency. …”
Get full text

Optimizing Locations of Primary Schools in Rural Areas of China by Yulong Chen

“…Finally, a practical case study was used to illustrate the application of the proposed mathematical model. The results showed that the traffic network has an important influence on the optimization location of rural schools, and the improvement of traffic network conditions can greatly reduce the time required for students to travel to school.…”
Get full text

Improved grey wolf optimizer for optimal reactive power dispatch with integration of wind and solar energy by F. Laouafi

“…The aim of this paper is to present a new improved grey wolf optimizer (IGWO) to solve the optimal reactive power dispatch (ORPD) problem with and without penetration of renewable energy resources (RERs). …”
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

Inverse Kinematics: Identifying a Functional Model for Closed Trajectories Using a Metaheuristic Approach by Raúl López-Muñoz, Mario A. Lopez-Pacheco, Mario C. Maya-Rodriguez, Eduardo Vega-Alvarado, Leonel G. Corona-Ramírez

“…Additionally, a method to identify a functional model that describes the effector trajectories is introduced using the same optimization technique. …”
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

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

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

A new type of sustainable operation method for urban rail transit: Joint optimization of train route planning and timetabling by Guorong Fan, Chao Li, Xinyun Shao, Fangzheng Zhen, Yao Huang

“…A linearization method for the model is proposed. To solve large-scale problems, an improved adaptive large neighborhood search algorithm (ALNS) is designed accordingly. …”
Get full text

Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load by Chao Xing, Jiajie Xiao, Xinze Xi, Jingtao Li, Peiqiang Li, Shipeng Zhang

“…The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. …”
Get full text

Operation Optimization Strategy of Commercial Combined Electric Heating System Based on Particle Swarm Optimization Algorithm by WANG Qing, LI Congcong, WANG Pingxin, WU Qingqing, CAI Xiaoyu

“… In order to improve the energy efficiency of the electric heating system, a particle swarm optimization (PSO, Particle Swarm Optimization)-based operation optimization strategy for the direct storage combined electric heating system is proposed.A mathematical model of influencing factors inside and outside the walls of electric heating buildings is established, and the simulink toolbox in matlab is used to build the overall system under the premise of determining the quantity of electric heating.Combining demand response ideas, the objective function is to establish the minimum heating and electricity cost of the user, and different sub-modules are selected to form the control module to achieve simulation verification, and the inverse cosine method is used to update the improved particle swarm algorithm to update the learning factor to solve the set objective function.Finally, through a calculation example of electricity consumption data of an enterprise in Jinan, Shandong, comparing energy consumption and economy can be obtained: the total energy consumption throughout the day is lower than the actual energy consumption, and the electricity bill is reduced by 17.16% compared with the unoptimized time.…”
Get full text

Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…[Objectives] To enhance water quality prediction accuracy, this study aims to address the following challenges: (1) traditional prediction methods often rely on simple, elementary decomposition techniques, limiting their ability to extract meaningful data features. (2) Single models and basic optimization algorithms result in low prediction accuracy. (3) Most approaches fail to leverage the advantages of different networks to analyze components of varying complexity, leading to inefficient model utilization. (4) Few studies incorporate error correction after prediction. …”
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

Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms by Nian Liu, Yuehan Zhao

“…Methodology In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. …”
Get full text