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object optimization » objective optimization (Expand Search), objectives optimization (Expand Search), robust optimization (Expand Search)
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Showing 1 - 20 results of 47 for search '(( binary based object optimization algorithm ) OR ( gene used codon optimization algorithm ))', query time: 0.50s Refine Results
  1. 1
    The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms.

    The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

    Published 2025
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  2. 2
    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>.

    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>. by Benjamin Gallois (10131683)

    Published 2021
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  3. 3
    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX

    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

    Published 2022
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  4. 4
    Table5_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table5_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  5. 5
    Image2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  6. 6
    Table1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  7. 7
    Image1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  8. 8
    Image3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  9. 9
    Table3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  10. 10
    Table4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  11. 11
    Table2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  12. 12
    Image4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…However, the stAI method employed a hill climbing algorithm to optimize the S<sub>ij</sub> weights, which is not ideal for obtaining the best set of S<sub>ij</sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. …”
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  13. 13
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
    “…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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  14. 14
    Comparisons between ADAM and NADAM optimizers.

    Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

    Published 2025
    “…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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  15. 15
    Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx

    Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx by Changjiang Liang (21099887)

    Published 2025
    “…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…”
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  16. 16
    SHAP bar plot.

    SHAP bar plot. by Meng Cao (105914)

    Published 2025
    “…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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  17. 17
    Sample screening flowchart.

    Sample screening flowchart. by Meng Cao (105914)

    Published 2025
    “…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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  18. 18
    Descriptive statistics for variables.

    Descriptive statistics for variables. by Meng Cao (105914)

    Published 2025
    “…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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  19. 19
    SHAP summary plot.

    SHAP summary plot. by Meng Cao (105914)

    Published 2025
    “…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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  20. 20
    ROC curves for the test set of four models.

    ROC curves for the test set of four models. by Meng Cao (105914)

    Published 2025
    “…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
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