From: Biochemical systems identification by a random drift particle swarm optimization approach
Results for the Experiments with Noise-free Data | ||||||
---|---|---|---|---|---|---|
Algorithms | RDPSO | SS method | ( μ, λ )-ES | PSO | DE | ( μ + λ )-ES |
Best Value of J | 0.009124 | 7.1358e-07 | 0.022858 | 7.140163 | 10.168989 | 0.123209 |
Mean Value of J | 0.178881 | 3.4.274e-06 | 0.736311 | 10.3859 | 17.701876 | 2.141820 |
Standard Deviation of J | 0.252749 | 1.3649e-06 | 0.960729 | 3.1927 | 4.112377 | 1.692726 |
CPU time(h) | 52.4 | -- | 54.9 | 49.2 | 53.8 | 53.3 |
Results for the Experiments with Noisy Data | ||||||
Algorithms | RDPSO | SS method | ( μ , λ )-ES | PSO | DE | ( μ + λ )-ES |
Best Value of J | 0.2313 | 0.2337 | 2.5957 | 7.7433 | 11.7900 | 5.1490 |
Mean Value of J | 0.3459 | 0.3106 | 3.6029 | 11.2353 | 18.5928 | 10.8691 |
Standard Deviation of J | 0.1268 | 0.1325 | 0.1730 | 3.2921 | 3.3616 | 3.7065 |
CPU time(h) | 52.4 | -- | 54.9 | 49.2 | 53.8 | 53.3 |