| constraint_penalty {MOEADr} | R Documentation |
"Penalty" constraint handling method for MOEA/D
Description
Uses the Penalty Function constraint handling method to generate a preference index for the MOEADr framework.
Usage
constraint_penalty(B, bigZ, bigV, beta, ...)
Arguments
B |
Matrix of neighborhoods (generated by |
bigZ |
Matrix of scalarized objective values for each neighborhood and
the incumbent solution (generated by |
bigV |
Matrix of violation values for each neighborhood and the
incumbent solution (generated in |
beta |
Penalization constant (non-negative value) |
... |
other parameters (unused, included for compatibility with generic call) |
Details
This function calculates the preference index of a set of neighborhoods
based on the "penalty" constraint handling method. Please
see order_neighborhood() for more information on the preference index
matrix.
Value
[ 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) + beta * v(xk), where f(xk) is the aggregation function value of
the k-th solution being compared, and v(xk) is its total constraint violation
(calculated in evaluate_population()$V$v).
References
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