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An improved multiple adaptive neuro fuzzy inference system based on genetic algorithm for energy management system of island microgrid
Published 2025-05-01“…EMS is a control system integrated within MGs for managing the operations of these DGs effectively to fulfill a power balance between power production and load demand in the most optimal way, especially in island MGs. In this paper, an EMS based on Multiple Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (MANFIS-GA) is proposed for PV/Wind/Diesel Generator/Battery (PWDB) island MG system, to optimize the output power of diesel generator, manage charging-discharging operation of MG Battery Storage keeping its State of Charge (SOC) in acceptable limits, and improve the MG system reliability and stability by mitigating the effects of sudden changes in the electrical loading and Renewable energy sources (RES) Power. …”
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763
Multi-objective layout optimization of hospital outpatient clinics based on NSGA II
Published 2025-04-01“…Abstract This study utilizes an improved NSGA-II algorithm to conduct a multi-objective optimization of the hospital outpatient department layout. …”
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764
Internet of Things Based Application Placement Technique in Fog Environment
Published 2025-05-01“…A pre-scheduling method is developed to efficiently allocate tasks by analyzing workflows to reduce computation delays and energy usage. Leveraging an Improved Memetic Algorithm (IMA), this strategy enables effective scheduling of parallel IoT workflows across fog and cloud servers, ensuring balanced resource utilization and enhanced scalability. …”
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Optimizing laser powder bed fusion parameters for enhanced hardness of Ti6Al4V alloys: A comparative analysis of metaheuristic algorithms for process parameter optimization
Published 2025-04-01“…Given its simplicity alongside its accuracy and robust performance, the JAYA algorithm proves the most appropriate method for LPBF parameter optimization. …”
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767
Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks
Published 2025-05-01“…The methodology consists of three key phases: (1) Data preprocessing, where missing values are handled using the multiple imputations by chain equation (MICE) technique and feature scaling is applied using standard and min-max scalers; (2) Feature selection, where the FFXO algorithm reduces feature dimensionality to enhance classification efficiency; and (3) Lung tumor classification, utilizing Bi-GAN to improve predictive accuracy. …”
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768
Intelligent adjustment and energy consumption optimization of the fresh air system in hospital buildings based on Fuzzy Logic and Genetic Algorithms
Published 2024-12-01“…This algorithm demonstrates the highest stability and consistency, the fastest processing speed, and the shortest response time, proving its superior performance in energy consumption management and cost optimization. …”
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769
Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches
Published 2025-05-01“…A multidisciplinary approach was adopted to optimize production processes, reduce manufacturing costs, and shorten processing times. …”
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770
Quad Rapidly-Exploring Random Tree Star Algorithm With Improved Potential Force for Unmanned Aerial Vehicle Path Planning
Published 2025-01-01“…Rapidly-exploring random tree star (RRT*) has attracted intensive attention in track planning due to its asymptotic optimal properties. However, the RRT* algorithm plans costly trajectory paths. …”
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771
Beyond boundaries: AI-optimized global landslide susceptibility mapping
Published 2025-12-01“…This study addresses these gaps by developing an optimized framework using support vector regression (SVR) enhanced with meta-heuristic algorithms (grey wolf optimizer [GWO] and bat algorithm) to refine model hyper-parameters. …”
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772
Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework
Published 2025-01-01“…The study focuses on maximizing expected monetary value (EMV) and the net present value of RM4 considering economic uncertainty (NPVeco of RM4), of the most pessimistic scenario among the RMs. The optimization variables are location, type (injection or production), and number of wells, while the non-dominated sorting genetic algorithm II (NSGA-II) is employed for multi-objective optimization. …”
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773
A novel feature selection algorithm using decomposition based multi-objective guided honey badger algorithm (MO-GHBA) and NSGA-III
Published 2023-04-01“…In most of the MOEAs based feature selection algorithms, more optimal solutions are obtained around the Pareto front's center because of the deficiency in selection features. …”
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774
Optimization Models for Reducing the Air Pollutants Emission in the Production of Insulation Bituminous
Published 2023-05-01“…According to the optimization results, the most suitable air temperature and percent excess air were selected to achieve the lowest pollutant emissions. …”
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775
A Framework for Low-Carbon Container Multimodal Transport Route Optimization Under Hybrid Uncertainty: Model and Case Study
Published 2025-06-01“…Subsequently, a multi-strategy improved whale optimization algorithm (WOA) is developed to solve the formulated model. …”
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776
An improved lightweight tiny-person detection network based on YOLOv8: IYFVMNet
Published 2025-04-01“…This operation also reduces the computational cost by decreasing the amount of required feature map channels, while maintaining the effectiveness of the feature representation. (3) he Minimum Point Distance Intersection over Union loss function is employed to optimize bounding box detection during model training. (4) to construct the overall network structure, the Layer-wise Adaptive Momentum Pruning algorithm is used for thinning.ResultsExperiments on the TinyPerson dataset demonstrate that IYFVMNet achieves a 46.3% precision, 30% recall, 29.3% mAP50, and 11.8% mAP50-95.DiscussionThe model exhibits higher performance in terms of accuracy and efficiency when compared to other benchmark models, which demonstrates the effectiveness of the improved algorithm (e.g., YOLO-SGF, Guo-Net, TRC-YOLO) in small-object detection and provides a reference for future research.…”
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777
A Q-Learning Crested Porcupine Optimizer for Adaptive UAV Path Planning
Published 2025-06-01“…To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. …”
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778
LPBSA: Pre-clinical data analysis using advanced machine learning models for disease prediction
Published 2025-06-01“…The current study introduces an optimization algorithm, Learner Performance-Based Behavior with Simulated Annealing (LPBSA), integrated with Multilayer Perceptron (MLP) as a neural network technique to improve disease prediction accuracy. …”
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Application of Multi-Strategy Controlled Rime Algorithm in Path Planning for Delivery Robots
Published 2025-07-01“…This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. …”
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780
VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection
Published 2025-12-01“…It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification.…”
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