يعرض 61 - 80 نتائج من 88 نتيجة بحث عن '(( binary based function optimization algorithm ) OR ( binary based ai optimization algorithm ))*', وقت الاستعلام: 0.45s تنقيح النتائج
  1. 61

    Schematic of iteration process of IDE-IIGA. حسب Ling Zhao (111365)

    منشور في 2025
    "…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …"
  2. 62

    Schematic diagram of IGA chromosome coding. حسب Ling Zhao (111365)

    منشور في 2025
    "…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …"
  3. 63

    Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization حسب Sandeep Jagonda Patil (22048337)

    منشور في 2025
    "…To tackle these challenges, this paper proposes the Blockchain Based Trusted Distributed Routing Scheme for MANET using Latent Encoder Coupled Generative Adversarial Network Optimized with Binary Emperor Penguin Optimizer (LEGAN-BEPO-BCMANET). …"
  4. 64

    S and V shaped transfer functions. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  5. 65

    S- and V-Type transfer function diagrams. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  6. 66

    Datasets and their properties. حسب Olaide N. Oyelade (14047002)

    منشور في 2023
    "…The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"
  7. 67

    Parameter settings. حسب Olaide N. Oyelade (14047002)

    منشور في 2023
    "…The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"
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  13. 73

    Pseudo Code of RBMO. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  14. 74

    P-value on CEC-2017(Dim = 30). حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  15. 75

    Memory storage behavior. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  16. 76

    Elite search behavior. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  17. 77

    Description of the datasets. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  18. 78

    Collaborative hunting behavior. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  19. 79

    Friedman average rank sum test results. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
  20. 80

    IRBMO vs. variant comparison adaptation data. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"