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Urban Microcirculation Traffic Network Planning Method Based on Fast Search Random Tree Algorithm
Published 2024-12-01Get full text
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Automated guided vehicle (AGV) path optimization method based on improved rapidly-exploring random trees
Published 2025-06-01“…In response to the issues of low computational efficiency, slow convergence speed, curvy paths, and the tendency to fall into local optima in rapidly-exploring random tree (RRT) algorithms for automated guided vehicle (AGV) path planning, this article proposes an improved RRT algorithm that combines adaptive step-size optimization with K-dimensional tree (KD-Tree) based fast nearest neighbor search. …”
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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses
Published 2024-06-01Get full text
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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method
Published 2025-06-01“…In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. …”
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Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning
Published 2024-12-01Get full text
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Research on vehicle scheduling for forest fires in the northern Greater Khingan Mountains
Published 2025-01-01“…Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm’s solution efficiency and accuracy, through the northern pristine forest area of Daxing’anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program. …”
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Smart Chaining: Templing and Temple Search
Published 2025-01-01“…Experimental evidence illustrates the dominance of Templing and Temple Search as it delivers <inline-formula> <tex-math notation="LaTeX">$4.5x$ </tex-math></inline-formula> faster searches and <inline-formula> <tex-math notation="LaTeX">$3.5x$ </tex-math></inline-formula> faster insertions over plain chaining at equal space complexity. …”
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Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data
Published 2025-02-01“…Seven machine learning algorithms were created and tested: Decision Tree (DT), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Stochastic Gradient Boosting (SGB), and K-Nearest Neighbors (KNN). …”
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Streamlining ICI Transformed as a Nonnegative System
Published 2025-07-01“…The previous algorithm used initial conditions that were randomly assorted pixel intensities. …”
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Illustration Design Model with Clustering Optimization Genetic Algorithm
Published 2021-01-01Get full text
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Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT*
Published 2025-01-01“…Aiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. …”
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Routing algorithm for heterogeneous computing force requests based on computing first network
Published 2025-02-01“…An optimized genetic algorithm to address this issue was proposed. This algorithm was designed from both local and global perspectives: to ensure fast convergence to the target solution locally, a single parameter satisfying the randomness strategy was used to initialize the population, making it widely dispersed in the solution space; adopting a multi-parameter solution (or path) balanced selection strategy for selection operations, making the selected population rich and diverse; adopting a two-layer crossover strategy for crossover operations, with the aim of expanding the breadth of global search; adopting a multi parameter random single point mutation strategy for mutation operations, with the aim of deepening local search capabilities. …”
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A revamped black winged kite algorithm with advanced strategies for engineering optimization
Published 2025-05-01“…Initially, the technique uses a logistic map for population initialization, swapping random generation to enhance global search effectiveness and fast convergence. …”
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Path planning algorithm based on the improved Informed-RRT* using the sea-horse optimizer
Published 2025-02-01“…ObjectiveIn order to solve the problems of random sampling, inefficient search, and difficulty in providing optimal paths in complex environments faced by traditional Informed-RRT* algorithms, an improved Informed-RRT* path planning algorithm based on the sea-horse optimizer (SHO) was proposed.MethodsThis algorithm combined the strengths of Informed-RRT* and SHO. …”
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Dye-cleaning prediction with a variant of nature-inspired algorithms coupled with extreme gradient boosting
Published 2025-07-01“…Hyperparameter tuning via differential evolution (DE), genetic algorithm (GA), random search (RS), and grid search (GS) with the XGBoost model was conducted to achieve more accurate results. …”
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Set-Based Differential Evolution Algorithm Based on Guided Local Exploration for Automated Process Discovery
Published 2020-01-01“…There are three major innovations in this work. First of all, a hybrid evolutionary strategy is proposed, in which a differential evolution algorithm is employed to search the solution space and rapidly approximate the optimal solution firstly, and then a specific local exploration method joins to help the algorithm skip out the local optimum. …”
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Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task.
Published 2025-01-01“…Secondly, the WOA's position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. …”
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