Search alternatives:
design optimization » bayesian optimization (Expand Search)
policy optimization » topology optimization (Expand Search), wolf optimization (Expand Search), process optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
where policy » three policy (Expand Search), weak policy (Expand Search), horn policy (Expand Search)
design optimization » bayesian optimization (Expand Search)
policy optimization » topology optimization (Expand Search), wolf optimization (Expand Search), process optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
where policy » three policy (Expand Search), weak policy (Expand Search), horn policy (Expand Search)
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1
Proposed Algorithm.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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2
Comparisons between ADAM and NADAM optimizers.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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DE algorithm flow.
Published 2025“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”
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5
Test results of different algorithms.
Published 2025“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”
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6
MSE for ILSTM algorithm in binary classification.
Published 2023“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”
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7
An Example of a WPT-MEC Network.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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8
Related Work Summary.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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9
Simulation parameters.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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10
Training losses for N = 10.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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11
Normalized computation rate for N = 10.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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12
Summary of Notations Used in this paper.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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13
Algorithm for generating hyperparameter.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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Results of machine learning algorithm.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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16
ROC comparison of machine learning algorithm.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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17
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Best optimizer results of Lightbgm.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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19
Best optimizer results of Adaboost.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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20
Best optimizer results of Lightbgm.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”