Reconstruction errors and their variability due to stochastic steps in the algorithms. by Hanane Omichessan (4991552)

Schematic diagram of dynamic step size expansion. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

The number of inactive nodes as a function of time steps in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)

Network structure of perception fusion algorithm. by Weilin Wu (13736)

Subjects:

The network lifetime as a function of number of sensor nodes. by Zhou He (10330007)

Delivered packets as a function of number of sensor nodes. by Zhou He (10330007)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

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

Subjects:

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

Statistical results of various algorithms. by ZeSheng Lin (20501356)

Subjects:

Images of partial benchmark functions. by ZeSheng Lin (20501356)

Subjects:

Fitness function parameters. by Ankita Sharma (3183054)

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …”

Algorithm parameters. by Ruyi Dong (9038174)

“…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”