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design optimization » bayesian optimization (Expand Search)
total sampling » total sample (Expand Search), data sampling (Expand Search), quota sampling (Expand Search)
binary task » binary mask (Expand Search)
task design » based design (Expand Search)
design optimization » bayesian optimization (Expand Search)
total sampling » total sample (Expand Search), data sampling (Expand Search), quota sampling (Expand Search)
binary task » binary mask (Expand Search)
task design » based design (Expand Search)
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Normalized computation rate for N = 10.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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22
Summary of Notations Used in this paper.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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23
Data Sheet 1_AutoRA: an innovative algorithm for automatic delineation of reference areas in support of smart soil sampling and digital soil twins.pdf
Published 2025“…In this study, we introduce the autoRA algorithm, an innovative automated soil sampling design method that utilizes Gower’s Dissimilarity Index to delineate RAs automatically. …”
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24
Pseudo Code of RBMO.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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25
P-value on CEC-2017(Dim = 30).
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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26
Memory storage behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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27
Elite search behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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28
Description of the datasets.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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29
S and V shaped transfer functions.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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30
S- and V-Type transfer function diagrams.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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31
Collaborative hunting behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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32
Friedman average rank sum test results.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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33
IRBMO vs. variant comparison adaptation data.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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34
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35
Free vibration analysis and optimal design of adhesively bonded double-strap joints by using artificial neural networks
Published 2021“…The effects of the adhesive material properties and joint geometrical parameters on the joint dynamic characteristics were investigated in detail using the trained ANNs. The optimum design problem is defined as a multi-objective optimization problem considering maximizing the first natural frequency and corresponding loss factor while minimizing the total structural weight. …”
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36
Optimal contrast analysis with heterogeneous variances and budget concerns
Published 2019“…Optimal allocation procedures for the Welch-Satterthwaite tests of standardized and unstandardized contrasts are presented to minimize the total sample size with the designated ratios, to meet a desirable power level for the least cost, and to attain the maximum power performance under a fixed cost. …”
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37
Results of network meta-analysis.
Published 2023“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”
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38
Results of network meta-analysis.
Published 2023“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”
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39
Results of network meta-analysis.
Published 2023“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”
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40
Results of network meta-analysis.
Published 2023“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”