Flowchart of the IFPA algorithm. حسب Yiqiong Gao (16698968)

"…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …"

Loss function and accuracy curve comparison. حسب Dayu Zhang (710629)

Mean OP score on the visual tracker benchmark dataset and its challenging sub-categories for the top ten tracking algorithms[1,2,3,4,5,21,11,8,7,6]. حسب Mingqi Luo (8703627)

Algorithm parameter settings. حسب Tao Dong (15551)

"…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …"

Algorithm of the main experiment targeted to measure the perceptual point spread function (pPSF) treating patients visual system including its optics, physiology and psychology as an integrated imaging system, and patient’s perceptions as its output signal. حسب Krzysztof Petelczyc (3954203)

"…<p>In the algorithm, the following variables were used: “Ic” denotes the intensity of the central diode (Ic = 40 cd); “DIST(i)” is a randomly sorted list of “D” angular stimuli positions distributed equally as a function of distance from 0.24° to 7.67° from the central point (D = 10), while “i” is an index corresponding to the current distance of a probe diode (“d”); “N” denotes the number of trials for each stimuli position (N = 20); “s” denotes the perceptual brightness value transformed to diode luminous intensity by an array “I(s)” corresponds to the table “scale (level)” determined by the algorithm presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0306331#pone.0306331.g003" target="_blank">Fig 3</a>; “cnt” is a counter of trials for the current probe diode’s distance, array threshold (d), and slope (d), i.e., it denotes the intensity of the single point of the pPSF and its uncertainty. …"

PSO parameters and objective function. حسب André G. Coimbra (21354135)

GA parameters and objective function. حسب André G. Coimbra (21354135)

Standard benchmark functions [42]. حسب Junhao Wei (6816803)

"…Thus, an enhanced Whale Optimization Algorithm (LSWOA) based on multiple strategies is proposed, aiming to overcome the limitations of the canonical WOA. …"

An example of the movement of a particle in a two-dimensional space based on the PSO algorithm in a single iteration. حسب Yury Chernyak (2138635)

Objective function value statistics. حسب Chao Li (145513)

F4 function and convergence curve graph. حسب Jiangli Yu (4601986)

F5 function and convergence curve graph. حسب Jiangli Yu (4601986)

F1 function and convergence curve graph. حسب Jiangli Yu (4601986)

F6 function and convergence curve graph. حسب Jiangli Yu (4601986)

F7 function and convergence curve graph. حسب Jiangli Yu (4601986)

F10 function and convergence curve graph. حسب Jiangli Yu (4601986)

F9 function and convergence curve graph. حسب Jiangli Yu (4601986)

F2 function and convergence curve graph. حسب Jiangli Yu (4601986)

F8 function and convergence curve graph. حسب Jiangli Yu (4601986)

F3 function and convergence curve graph. حسب Jiangli Yu (4601986)