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

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A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

Comparison of average reward values in various simulation scenarios. by Honglei Pang (22693724)

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-value on CEC2022 (dim = 20). by Yuqi Xiong (12343771)

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Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

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Distribution of objective function values solved by three algorithms. by Meilin Zhu (688698)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

Objective function values of different algorithms on individual problem instances. by Panfei Li (22379086)

“…<p>Objective function values of different algorithms on individual problem instances.…”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Sensor value scenario for average value algorithm. by Gourab Saha (8987405)

Results obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

Performance profile of the four algorithms based on the function value of iteration number. by Sulaiman M. Ibrahim (20614376)

“…<p>Performance profile of the four algorithms based on the function value of iteration number.…”

(a) Radar chart of these algorithms (23 Benchmark functions), (b) The sorting diagram of these algorithms (23 Benchmark functions). by Yu Liu (6938)

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Convergence curves obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Different algorithms in 23 benchmark functions to obtain partial boxplot. by Yuqi Xiong (12343771)

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Results obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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Comparison of PSNR values of different algorithms. by Xiaojun Wang (50332)

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Changes in FID values of different algorithms. by Xiaojun Wang (50332)

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Comparison of SSIM values of different algorithms. by Xiaojun Wang (50332)

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