Pseudo-code of ABC algorithm. by Jianpeng Zhang (528185)

“…The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. …”

Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₂. by Hui Huang (4122)

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Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₅. by Hui Huang (4122)

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Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₃. by Hui Huang (4122)

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Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₄. by Hui Huang (4122)

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Performance of the greedy approximation algorithm. by Virgile Andreani (17087764)

The empirical distribution function and fitted distribution functions for data sets <i>Q</i> and <i>T</i>. by Salem A. Alyami (10572611)

Function <i>wVL</i> for evaluation of resettable bistability. by Takashi Naka (272391)

Gaussian function surface with different σs. by JianWu Long (10731271)

Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

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Conservation and functional variation across TcdB subtypes. by Michael J. Mansfield (9922934)

Greedy optimization algorithm. by Alexis Bénichou (19832361)

Fitting results of the different optimization algorithms applied. by Sonja Langthaler (6069149)

Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems by Dongge Lei (6836915)

“…The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.…”

The confusion matrix of the different algorithms. by Qiushi Wang (417314)

Convergent curves of EOSA and related optimization algorithms benchmark functions over 1, 50, 100, 200, 300, 400 and 500 epochs. by Tehnan I. A. Mohamed (16846175)

“…<p>Convergent curves of EOSA and related optimization algorithms benchmark functions over 1, 50, 100, 200, 300, 400 and 500 epochs.…”

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Flowchart of improved FOX optimization algorithm. by Mahmood A. Jumaah (22272371)

“…Additionally, it achieved 880 wins, 228 ties, and 348 losses against 16 optimization algorithms across all involved functions and problems. …”

Functional annotations of peaks. by Azusa Tanaka (540528)