Search alternatives:
based optimization » whale optimization (Expand Search)
primary aim » primary care (Expand Search), primary data (Expand Search)
tasks based » task based (Expand Search), cases based (Expand Search)
aim based » ai based (Expand Search), bim based (Expand Search), aom based (Expand Search)
based optimization » whale optimization (Expand Search)
primary aim » primary care (Expand Search), primary data (Expand Search)
tasks based » task based (Expand Search), cases based (Expand Search)
aim based » ai based (Expand Search), bim based (Expand Search), aom based (Expand Search)
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Routing policy based on path satisfaction.
Published 2025“…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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Proposed Algorithm.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Comparisons between ADAM and NADAM optimizers.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Flowchart of simple ant colony algorithm.
Published 2025“…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement
Published 2019“…The primary aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based trajectory optimization of human movement. …”
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The Pseudo-Code of the IRBMO Algorithm.
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. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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IRBMO vs. meta-heuristic algorithms boxplot.
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. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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IRBMO vs. feature selection algorithm boxplot.
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. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Business priorities.
Published 2025“…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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Topology of 14-node communication network.
Published 2025“…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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Changes of risk value under different parameters.
Published 2025“…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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Performance of active and standby paths.
Published 2025“…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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DATA.
Published 2025“…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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Fig 9 -
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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Predictive performance indicators.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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Fig 8 -
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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GBO procedure.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”