scalarization_wt: Weighted Tchebycheff Scalarization

In MOEADr: Component-Wise MOEA/D Implementation

Weighted Tchebycheff Scalarization

Description

Perform Weighted Tchebycheff Scalarization for the MOEADr package.

Usage

scalarization_wt(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 Weighted Tchebycheff method.

Value

Vector of scalarized performance values.

References

Q. Zhang and H. Li, "MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition", IEEE Trans. Evol. Comp. 11(6): 712-731, 2007.

H. Li, Q. Zhang, "Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II", IEEE. Trans. Evol. Comp. 12(2):284-302, 2009.

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_wt(Y, W, minP)

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