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Convergence curve of unimodal functions compared with the meta-heuristic algorithms.
Published 2023Subjects: -
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Convergence curve of others functions compared with the meta-heuristic algorithms.
Published 2023Subjects: -
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Convergence curve of multimodal functions compared with the meta-heuristic algorithms.
Published 2023Subjects: -
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Convergence curve of multimodal functions compared with the PSO family algorithms.
Published 2023Subjects: -
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Algorithm operation steps.
Published 2025“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”
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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results
Published 2025“…A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …”
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<b>Opti2Phase</b>: Python scripts for two-stage focal reducer
Published 2025“…</p><p dir="ltr">The package includes:</p><ul><li>Scripts for first-order analysis, third-order modeling, optimization using a Physically Grounded Merit Function (PGMF), and RMS-based refinement.…”
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FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz
Published 2025Subjects: -
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The losses of the training process before and after the optimization of loss function.
Published 2022Subjects: -
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Rosenbrock function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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Rosenbrock function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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Levy function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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Rastrigin function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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Levy function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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Rastrigin function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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39
Levy function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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Levy function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”