APSO–CNN algorithm’s minimal fitness values modified over iterations. by Ahmed Bahaa (12887063)

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

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

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Comparison of the WHO and IERWHO algorithms improvement points. by WanRu Zhao (18980374)

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The <i>min</i> obtained by DSRABSQL, S2V-DQN and 2-opt-DRL algorithms for 25 instances. by Jin Zhang (53297)

Sensitivity analysis of the TV-BayesOpt algorithm performance at tracking a periodic temporal drift when the period of the drift is offset from the covariance function period antic... by John E. Fleming (8533956)

The SSIM for the different algorithms. by Bingbing Li (461702)

“…Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. …”

Average running time line graph of different algorithms. by Wuyi Li (2984439)

LF and SP optimum values using the PSOGSAHO algorithm. by Nada Ahmed Ezz-Eldien (17396553)

Subjects: “…gravitational search algorithm…”

Detailed information of benchmark functions. by Guangwei Liu (181992)

“…In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. …”

Schematics for gmxapi operations. by M. Eric Irrgang (4487278)

Heatmap results from the global sensitivity analysis for the generation of villous vessels within a placentone. by Diana C. de Oliveira (9220729)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Example of an application of functional safety-critical energy optimization reallocation tasks. by Zhiqun Lin (1429501)

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Classification algorithm implemented in SPARTA. by Baptiste Ruiz (20285217)

APSO–WOA–CNN algorithm’s minimal fitness values modified over iterations. by Ahmed Bahaa (12887063)

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Algorithm comparison experiment on the NWPU VHR-10 dataset. by Pingping Yan (462509)

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Algorithm parameter levels. by Peng Yao (448155)

“…Alternatively, the formulated objective functions often deviate from real-world scenarios. …”

Experimental optimization results for dimension 10 using reported competitor algorithms on the unimodal, multimodal, hybrid, and composite of CEC2022 benchmark functions. by WanRu Zhao (18980374)

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<i>S</i>_<i>c</i> of ch150, rat575 and fl1400 for algorithms of each group. by Jin Zhang (53297)