APSO–WOA–CNN and FNN algorithms’ accuracy training sets. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and FNN algorithms’ accuracy validation sets. by Ahmed Bahaa (12887063)

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Convergence curve of IWOA-SMC-ALADRC fitness function. by Ziwei Wu (11386352)

Comparison of salary forecast results of different NN algorithms. by Yangda Gong (12015173)

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Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Fixed-dimensional multimodal reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Comparative results of different detection algorithms. by Xiaozhou Feng (2918222)

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Description of the 30 IEEE CEC 2014 benchmark functions. by Qiong Wu (84327)

IEEE CEC 2022 benchmark function specifications. by Yang Gao (18005)

IEEE CEC 2017 benchmark function specifications. by Yang Gao (18005)

The validation set results under different split function. by Chengxin Yin (17283226)

The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms. by Hamed Khodadadi (21446172)

Comparison with the BST algorithm for the AF and CST bundles. by Thomas Durantel (20933812)

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Network utility for different desirability functions. by Maryam Chinipardaz (18098413)

Improved Ant Colony Algorithm by AOBEI ZHANG (17657703)

“…As the iteration progresses, we maintain a delicate equilibrium between exploration and exploitation, ensuring continued diversity while honing in on promising solutions. To achieve this, we integrate the hyperbolic tangent function, fine-tuning the ACO algorithm's behavior to adaptively adjust its search strategy across iterations.(2) Recognizing the tendency of heuristic algorithms to converge prematurely into local optima, we devise a max-min ant colony strategy. …”

Flowchart of a single epoch in the FOX optimization algorithm [10]. by Mahmood A. Jumaah (22272371)

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Adaptive exploration and exploitation method of Improved FOX optimization algorithm. by Mahmood A. Jumaah (22272371)

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Graphical representation of algorithm’s performance ranking. by Jeffrey O. Agushaka (11326679)

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Graphical representation of the algorithm’s performance score. by Jeffrey O. Agushaka (11326679)

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