Table 3 J values and computational time for the global optimization methods in Case Study 1
From: Biochemical systems identification by a random drift particle swarm optimization approach
Results for the Experiments (simulations) with Noise-free Data | ||||||
|---|---|---|---|---|---|---|
Algorithms | RDPSO | SS method | ( μ, λ )-ES | PSO | DE | ( μ+ λ )-ES |
Best Value of J | 1.3740e-14 | 0.3930 | 301.9941 | 3.4461e-005 | 258.892 | 339.4941 |
Mean Value of J | 2.5845e-004 | 0.5703 | 2.8522e+06 | 0.0225 | 2.3197e+03 | 1.9103e+06 |
Standard Deviation of J | 3.5978e-04 | 0.1348 | 2.2673e+06 | 0.0183 | 704.7776 | 2.2118e+06 |
CPU time(h) | 0.0347 | -- | 0.0419 | 0.0330 | 0.0353 | 0.0419 |
Results for the Experiments (simulations) with Noisy Data | ||||||
Algorithms | RDPSO | SS method | ( μ , λ )-ES | PSO | DE | ( μ + λ )-ES |
Best Value of J | 0.2023 | 0.7195 | 388.9242 | 0.2028 | 1.0273e+003 | 52.4817 |
Mean Value of J | 0.2026 | 0.8361 | 2.1952e+05 | 0.2083 | 2.5488e+003 | 8.2498e+05 |
Standard Deviation of J | 4.2918e-04 | 0.0743 | 3.2308e+05 | 0.0126 | 5393.6832 | 1.6245e+06 |
CPU time(h) | 0.0349 | -- | 0.0423 | 0.0338 | 0.0351 | 0.0422 |