| | DASA | PSO | DE |
|---|
Parameter |
m
|
Ρ
| s+ | s- |
S
|
K
|
w
|
c
|
P
|
ST R
|
F
|
CR
|
|---|
Lower | 4 | 0 | 0 | 0 | 4 | 1 | 0 | 1 | 6 | 1 | 0 | 0 |
Upper | 200 | 1 | 1 |
ρ
| 200 | S-1 | 1 | 4 | 200 | 10 | 2 | 1 |
Tuned | 144 | 0.036 | 0.573 | 0.01 | 155 | 89 | 0.762 | 1.037 | 81 | 8 | 0.942 | 0.915 |
- The table includes the search ranges (their lower and upper bound) for each of the four parameters of each of the three different meta-heuristic optimization methods that were tuned. We used the Sobol' sampling procedure with the number of sampling points set to 2000. The resulting vector of method's parameters was chosen as the one that showed best median performances (according to the SSE metric) in the multiple-run experiments among the 2000 sampled parameter settings. A single experiment included eight runs, each performed with half a million of objective function evaluations. The parameter tuning was performed on the complete observation scenario using noise-free artificial data.
