Table 5 Accuracy and precision metrics for multimodal benchmark functions with N = 30, Tmax = 1000 and Texp = 30.
From: Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
Function | Metrics | QPSO-LR | QPSO-RM | QPSO-CS | PSO | FFO | GA |
|---|---|---|---|---|---|---|---|
\(f_{9}\) | \(\omega\) | \(1.4137 \times 10^{2}\) | \(1.4873 \times 10^{2}\) | \(1.3877 \times 10^{2}\) | \(2.6002 \times 10^{2}\) | 90.5291 | \(1.8003 \times 10^{2}\) |
\(\alpha\) | 6.0517 | 6.7619 | 0.8768 | 28.8637 | 80.4336 | 90.3119 | |
\(f_{10}\) | \(\omega\) | 7.1803 | 9.1616 | 6.6542 | 2.4200 | 7.2001 | 14.7343 |
\(\alpha\) | 0.6570 | 1.1580 | 0.7452 | 0.0361 | 0.4907 | 6.6963 | |
\(f_{11}\) | \(\omega\) | 0.3426 | 0.3647 | 0.3127 | 0.2036 | 0.0001 | 0.0516 |
\(\alpha\) | 0.0116 | 0.0167 | \(1.688 \times 10^{- 4}\) | 0.0045 | 0.0001 | 19.2122 | |
\(f_{12}\) | \(\omega\) | 12.9950 | 14.1370 | 15.6384 | 5.2224 | 4.3928 | 30.8202 |
\(\alpha\) | 2.9750 | 3.7139 | 2.5028 | 0.0400 | 0.0820 | 19.2122 | |
\(f_{13}\) | \(\omega\) | \(1.0457 \times 10^{7}\) | \(8.9922 \times 10^{6}\) | \(1.3693 \times 10^{7}\) | \(1.2975 \times 10^{6}\) | \(9.9214 \times 10^{6}\) | \(4.5856 \times 10^{7}\) |
\(\alpha\) | \(4.6454 \times 10^{6}\) | \(4.2749 \times 10^{6}\) | \(3.1766 \times 10^{6}\) | \(2.7464 \times 10^{3}\) | \(2.8049 \times 10^{5}\) | \(4.1327 \times 10^{7}\) | |
\(f_{14}\) | \(\omega\) | \(1.9438 \times 10^{7}\) | \(1.9614 \times 10^{7}\) | \(2.7256 \times 10^{7}\) | \(3.4698 \times 10^{6}\) | \(1.6631 \times 10^{7}\) | \(7.3698 \times 10^{7}\) |
\(\alpha\) | \(7.4400 \times 10^{6}\) | \(8.2036 \times 10^{6}\) | \(7.1400 \times 10^{6}\) | \(1.3277 \times 10^{4}\) | \(5.1217 \times 10^{5}\) | \(6.1365 \times 10^{7}\) | |
\(f_{15}\) | \(\omega\) | 1.4280 | 1.6201 | 1.3598 | 0.5925 | 1.3838 | 2.7137 |
\(\alpha\) | 0.2589 | 0.1861 | 0.0947 | 0.0103 | 0.0773 | 1.2823 |
