Perform scalarization for the MOEADr package.
scalarize_values(normYs, W, B, aggfun)List generated by scale_objectives(), containing two matrices
of scaled objective values (normYs$Y and normYs$Yt) and two vectors,
containing the current estimates of the ideal (normYs$minP) and nadir
(normYs$maxP) points. See scale_objectives() for details.
matrix of weights, generated by generate_weights().
neighborhood matrix, generated by define_neighborhood().
List containing the aggregation function parameters. See
Section Scalar Aggregation Functions of the moead() documentation for
details.
[ (T+1) x N ] matrix of scalarized performance values. Each column
contains the T scalarized performances of the candidate solutions in the
neighborhood of a given subproblem, plus the scalarized performance value
for the incumbent solution for that subproblem.
This routine calculates the scalarized performance values for the MOEA/D.
The list of available scalarization methods can be generated using
get_scalarization_methods()
F. Campelo, L.S. Batista, C. Aranha (2020): The MOEADr Package: A
Component-Based Framework for Multiobjective Evolutionary Algorithms Based on
Decomposition. Journal of Statistical Software doi:10.18637/jss.v092.i06