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Improving the Solving of Optimization Problems: A Comprehensive Review of Quantum Approaches
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An improvement in the design process of sustainable peak power rating transformer for solar utility
Published 2025-09-01“…Such upgrades are essential for transitioning to a zero-emission electricity system and developing green energy projects.In this paper, a transformer has been studied using a combination of electrical design and 3D finite element method simulation to evaluate various design parameters. An optimization study has been conducted using an innovative multi-objective genetic algorithm utilizing a cost function that factors in size and material costs to identify the most efficient and cost-effective design solutions.The proposed design method was then validated through thermal model simulations and experimental tests based on the photovoltaic load cycle. …”
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Research on Cold Chain Logistics Joint Distribution Vehicle Routing Optimization Based on Uncertainty Entropy and Time-Varying Network
Published 2025-05-01“…The solution combines simulated annealing strategies with genetic algorithms. It also uses the entropy mechanism to optimize uncertainties, improving global search performance. …”
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Impact of an improved random forest-based financial management model on the effectiveness of corporate sustainability decisions
Published 2024-12-01“…In order to improve the generalization ability of financial management models, pruning methods were adopted in the study to avoid overfitting. …”
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Prediction of Earthquake Death Toll Based on Principal Component Analysis, Improved Whale Optimization Algorithm, and Extreme Gradient Boosting
Published 2025-08-01“…To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). …”
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Passenger Flow Prediction for Rail Transit Stations Based on an Improved SSA-LSTM Model
Published 2024-11-01“…According to the experimental results, the proposed SSA-LSTM model has a more effective performance than the Support Vector Regression (SVR) method, the eXtreme Gradient Boosting (XGBoost) model, the traditional LSTM model, and the improved LSTM model with the Whale Optimization Algorithm (WOA-LSTM) in the passenger flow prediction. …”
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IGWO-MSVR model for predicting stress in coal seam during drilling process
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Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm
Published 2025-04-01“…For the prediction of VIV parameters, the Random Forest model is the most effective. The RMSE values of the improved optimal algorithm are 0.017, 0.026, and 0.295, and the R<sup>2</sup> values are 0.9421, 0.8875, and 0.9462. …”
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An Optimal Longitudinal Control Strategy of Platoons Using Improved Particle Swarm Optimization
Published 2020-01-01“…An improved particle swarm optimization algorithm was used to optimize the weighting coefficients for the controller state and control variables. …”
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YOLOGX: an improved forest fire detection algorithm based on YOLOv8
Published 2025-01-01“…Finally, the proposed Focal-SIoU loss function replaces the original loss function, effectively reducing directional errors by combining angle, distance, shape, and IoU losses, thus optimizing the model training process. YOLOGX was evaluated on the D-Fire dataset, achieving a mAP@0.5 of 80.92% and a detection speed of 115 FPS, surpassing most existing classical detection algorithms and specialized fire detection models. …”
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Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm
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System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
Published 2025-03-01“…Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. …”
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A Novel Exploration Stage Approach to Improve Crayfish Optimization Algorithm: Solution to Real-World Engineering Design Problems
Published 2025-06-01“…In order to compensate these shortcomings, this study proposes an Improved Crayfish Optimization Algorithm (ICOA) that designs the competition stage with three modifications: (1) adaptive step length mechanism inversely proportional to the number of iterations, which enables exploration in early iterations and exploitation in later stages, (2) vector mapping that increases stochastic behavior and improves efficiency in high-dimensional spaces, (3) removing the X<sub>shade</sub> parameter in order to abstain from early convergence. …”
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Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis
Published 2021-01-01“…The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. …”
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SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm
Published 2025-04-01“…This paper presents a multi-head attention-driven intrusion detection with improved white shark optimization algorithm (MHAID-IWSOA) methodology in IoT networks. …”
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Site Selection Optimization Strategy for Independent Secondary Device Integrated Center Based on Improved Artificial Bee Colony Algorithm
Published 2021-09-01“…Taking the simple topology of a substation in a city’s urban area as an example, the effectiveness of the model is proved. Firstly, the possible interval of the optimal solution is calculated with the improved artificial bee colony algorithm; and then the ISDIC site node that is most suitable for the center is obtained through precise calculations; and finally the practical engineering significance of the model and the superiority of the method are fully illustrated through analyzing the indexes of the optimal node.…”
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