Construct the preference index matrix based only on performance values.
constraint_none(B, bigZ, bigV, ...)Matrix of neighborhoods (generated by define_neighborhood(...)))
Matrix of scalarized objective values for each neighborhood and the
incumbent solution (generated by scalarize_values)
Matrix of violation values for each neighborhood and the incumbent solution
other parameters (unused, included for compatibility with generic call)
[ N x (T+1) ] matrix of preference indices. Each row i contains
a permutation of {1, 2, ..., (T+1)}, where 1,...,T correspond
to the solutions contained in the neighborhood of the i-th subproblem,
B[i, ], and T+1 corresponds to the incumbent solution for that
subproblem. The order of the permutation is defined by the increasing values
of f(xk), where f(xk) is the aggregation function value of
the k-th solution being compared.
This function ignores the violation values when constructing the preference index matrix, using only the scalarized performance values.
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