RMSE values of different algorithms for different bands in the "PY" region. by YueSheng Jiang (19267984)

The comparison results of different algorithms on 23 benchmark functions with D=30. by Yu Li (183836)

The comparison results of different algorithms on CEC2017 functions with D=30. by Yu Li (183836)

Algorithm Parameter Setting. by Tong Zhou (3770)

“…This strategy uses the improved D3QN algorithm combined with the excellent smoothness of the sine function to evaluate driving safety, helping vehicles find suitable gaps in traffic flow. …”

Simulations details for the Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

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Simulations details for the Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

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Benchmark functions. by Mingjun Li (178925)

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The probability density function of points in feature space of variance versus mean of the local field for (0,3] and (0,3] displayed as a contour-plot. by Ali Talebi (7164203)

The parameter settings of compared algorithms. by Xuxu Zhong (10099054)

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A bar chart for the values of objective function—Phase I. by Fadi K. Dib (5204807)

A bar chart for the number of function evaluations—Phase I. by Fadi K. Dib (5204807)

A bar chart for the values of objective function—Phase II. by Fadi K. Dib (5204807)

A bar chart for the number of function evaluations—Phase III. by Fadi K. Dib (5204807)

Python cv coding screenshots of all developed fingerprints on distinct surfaces. by Nisha Yadav (366131)

Convergence curves for SGHHO and advanced algorithms on 9 selected benchmark functions. by Qiong Wu (84327)

Forward and inverse problems and misfit function. by Rutinaldo Aguiar Nascimento (14259594)

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The execution times (nano seconds) per particle are measured for four different algorithms [47], (A: first algorithm, B: second algorithm, C: third algorithm: and D: fourth algorit... by Abedalmuhdi Almomany (18547669)

Result of benchmark functions. by Mingjun Li (178925)

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Single-phase direct grounding of BPNN line. by Yunpeng Bai (1714087)

TV-BayesOpt algorithm performance for tracking a gradual drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”