The value ranges of position parameters. by Ahmed Bahaa (12887063)

Subjects:

The function value of the single objective optimal solution. by Yuhang Wang (332111)

Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…The article deals with contrast enhancement as an optimization problem and uses the Dragonfly Algorithm (DA) to find the optimal grey-level intensity values. …”

Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

“…<p dir="ltr">Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails". The supplemental figures are the data from the "Dibona" machine regarding the energy consumption of the algorithm. …”

Animations as supplemental material for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

Result of output membership functions of the truth value. by Thanasan Intarakumthornchai (9606684)

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

Subjects:

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

Subjects:

Parameter values and utility/disutility functions. by Jorge Herrera de la Cruz (11886266)

The results of the tested methods on 12 benchmarking functions of the STD values. by Chuang Zhang (2199421)

Benchmark functions. by Xiaowen Zhang (83669)

Subjects:

Changes in the loss function value on the validation set. by Kyung Won Kwon (18150416)

-value on 23 benchmark functions (dim = 30). by Yuqi Xiong (12343771)

Proposed algorithm mean and standard deviation (StDev) versus compared algorithms for the benchmark functions. by El-Sayed M. El-kenawy (14581088)

ITAE objective function values for all runs of en-CSA, CSA, RUN, PDO and RIME algorithms. by Sarah A. Alzakari (19704611)

“…<p>ITAE objective function values for all runs of en-CSA, CSA, RUN, PDO and RIME algorithms.…”

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)

“…<p>The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms.…”

Objective function values and CPU times of the considered solution approaches. by Yangjun Ren (17691912)

Objective function values and CPU times of the considered solution approaches. by Yangjun Ren (17691912)

Improve the weight coefficient of DWA evaluation function. by Peiying Li (797714)

Subjects:

Comparison of benchmark function results between traditional algorithms with 60 dimensions. by Xiaoyi Zhang (577299)