The convergence curve of each optimization algorithm under the test function. by Hao Yang (328526)

Layout of the primary 3D manufacturing cell facility [5]. by Yanlin Zhao (251552)

The robustness test results of the model. by Xini Fang (20861990)

Flow chart of the Improved Kepler Orbital Algorithm (IKOA). by Hao Yang (328526)

Pseudocode of artificial dragonfly algorithm. by Ghassan Ahmed Ali (17041488)

“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”

The Hopfield artificial neural network algorithm. by Ghassan Ahmed Ali (17041488)

“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”

Robustness Analysis of each model. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Data_Sheet_1_Robust optimization for casualty scheduling considering injury deterioration and point-edge mixed failures in early stage of post-earthquake relief.docx by Yufeng Zhou (3702208)

Algorithms and evaluation metrics integrated in the COPS framework. by Teemu J. Rintala (19333003)

Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

Flow diagram of Wan Abdullah method for HNN. by Ghassan Ahmed Ali (17041488)

“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”

Training error and accuracy for all HNN models. by Ghassan Ahmed Ali (17041488)

“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”

G<i>m</i>R performance of various HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”

<i>MAPE performance of various</i> HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”

RMSE performance of various HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

“…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”

MLP vs classification algorithms. by Mohd Mustaqeem (19106494)

“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. …”

S1 Data - by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Data_Sheet_1_Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.pdf by Wen Cao (696121)

Wilcoxon’s rank sum test results. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”