Iterative optimization convergence curve of each algorithm. by Guangwei Liu (181992)

The optimization results of each algorithm for the 8 benchmark functions. by Guangwei Liu (181992)

Optimal results of the different algorithms on the speed design reducer problem. by Guangwei Liu (181992)

Optimal results of the different algorithms on the pressure vessel design problem. by Guangwei Liu (181992)

Optimal results of the different algorithms on the gear train design problem. by Guangwei Liu (181992)

The optimization results of each algorithm for the CEC2019 benchmark functions. by Guangwei Liu (181992)

Optimal results of the different algorithms on the tension/compression spring design problem. by Guangwei Liu (181992)

Random parameter factor. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Nonlinear fast convergence factor. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Details of 23 basic benchmark functions. by Ying Li (38224)

Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Calibration plot of the apparent and optimism-corrected models. by Dominika Szabo (14129304)

Total primary energy demand by fuel in China. by Da Lian (18880882)

Wilcoxon’s rank sum test results. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Flowchart of MSHHOTSA. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

S1 Data - by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Tension/compression spring design problem. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Speed reducer design problem. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Flowchart of TSA [43]. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”

Pressure vessel design problem. by Guangwei Liu (181992)

“…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i>₁ to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”