Description Usage Arguments Details Value Author(s) See Also
View source: R/repairInfeasRI2.R
If the solution \vec{x} is infeasible, i.e. if there is any i or any j such that
g_i(\vec{x})>0 or |h_j(\vec{x})| - currentEps >0
:
Estimate the gradient of the constraint surrogate function(s) (go a tiny step in each dimension in the direction of constraint increase).
Take cobra$ri$mmax
random realizations in the 'feasible parallelepiped'
and select among them the best feasible solution, based on the surrogates,
Check whether the new solution is for every dimension in the bounds
[cobra$lower,cobra$upper]
of the search region.
If not, set the gradient to 0 in these dimensions and re-iterate from step 2.
There is no guarantee but a good chance, that the returned solution z
will be feasible.
1 | repairInfeasRI2(x, gReal, rbf.model, cobra, checkIt = FALSE)
|
x |
an infeasible solution vector \vec{x} of dimension |
gReal |
a vector (g_1(\vec{x}),…,g_m(\vec{x}), h_1(\vec{x}),…,h_r(\vec{x})) holding the real constraint values at \vec{x} |
rbf.model |
the constraint surrogate models |
cobra |
parameter list, we need here
|
checkIt |
[FALSE] if TRUE, perform a check whether the returned solution is really feasible. Needs access to the true constraint functions. |
For further details see [Koch15a] Koch, P.; Bagheri, S.; Konen, W. et al. "A New Repair Method For Constrained Optimization". Proc. 17th Genetic and Evolutionary Computation Conference (GECCO), 2015.
z
, a vector of dimension d
with a repaired (hopefully feasible) solution
Wolfgang Konen, Cologne University of Applied Sciences
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