MOEADr (version 1.1.3)

scalarization_pbi: Penalty-based Boundary Intersection Scalarization

Description

Perform PBI Scalarization for the MOEADr package.

Usage

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

Value

Vector of scalarized performance values.

Arguments

Y

matrix of objective function values

W

matrix of weights.

minP

numeric vector containing estimated ideal point

aggfun

list containing parameters for the aggregation function. Must contain the non-negative numeric constant aggfun$theta.

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 PBI method.

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 tools:::Rd_expr_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)
aggfun <- aggfun    <- list(name = "pbi", theta = 5)
Z      <- scalarization_pbi(Y, W, minP, aggfun)