Search alternatives:
guide optimization » guided optimization (Expand Search), driven optimization (Expand Search), whale optimization (Expand Search)
phase optimization » whale optimization (Expand Search), based optimization (Expand Search), path optimization (Expand Search)
image phase » image 1_case (Expand Search), image 2_case (Expand Search), image 3_case (Expand Search)
guide optimization » guided optimization (Expand Search), driven optimization (Expand Search), whale optimization (Expand Search)
phase optimization » whale optimization (Expand Search), based optimization (Expand Search), path optimization (Expand Search)
image phase » image 1_case (Expand Search), image 2_case (Expand Search), image 3_case (Expand Search)
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Descriptive analysis of the outcomes by the optimized LSTM using several optimization algorithms.
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Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm.
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Performance of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm.
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Analysis plots of the obtained results using the proposed AD-PSO-Guided WOA LSTM algorithm.
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The Pseudo-Code of the IRBMO Algorithm.
Published 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. …”
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IRBMO vs. meta-heuristic algorithms boxplot.
Published 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. …”
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IRBMO vs. feature selection algorithm boxplot.
Published 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. …”
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Pseudo Code of RBMO.
Published 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. …”
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P-value on CEC-2017(Dim = 30).
Published 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. …”