scalarization_awt: Adjusted Weighted Tchebycheff Scalarization

In MOEADr: Component-Wise MOEA/D Implementation

Adjusted Weighted Tchebycheff Scalarization

Description

Perform Adjusted Weighted Tchebycheff Scalarization for the MOEADr package.

Usage

scalarization_awt(Y, W, minP, eps = 1e-16, ...)

Arguments

Y	matrix of objective function values
W	matrix of weights.
minP	numeric vector containing estimated ideal point
eps	tolerance value for avoiding divisions by zero.
...	other parameters (included for compatibility with generic call)

Details

This routine calculates the scalarized performance values for the MOEA/D using the Adjusted Weighted Tchebycheff method.

Value

Vector of scalarized performance values.

References

Y. Qi, X. Ma, F. Liu, L. Jiao, J. Sun, and J. Wu, “MOEA/D with adaptive weight adjustment,” Evolutionary Computation, vol. 22, no. 2, pp. 231–264, 2013.

R. Wang, T. Zhang, and B. Guo, “An enhanced MOEA/D using uniform directions and a pre-organization procedure,” in IEEE Congress on Evolutionary Computation, Cancun, Mexico, 2013, pp. 2390–2397.

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

Examples

W    <- generate_weights(decomp = list(name = "sld", H = 19), m = 2)
Y    <- matrix(runif(40), ncol = 2)
minP <- apply(Y, 2, min)
Z    <- scalarization_awt(Y, W, minP)

MOEADr documentation built on Jan. 9, 2023, 1:24 a.m.