Generate a preset configuration for moead()].
preset_moead(name = NULL)name of the preset to be generated. Use preset_moead() to obtain
the list of available options.
List object containing the preset, to be used as an input to moead();
or, if name == NULL (the default), returns a logical flag invisibly.
This function returns a list of configuration presets taken from
the literature to be used with the moead() function in package MOEADr.
Use these configurations as a starting point. We strongly recommend that you play around with the particular configurations (see example).
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
# Generate list of available presets
preset_moead(name = NULL)
#> name x description
#> 1 original | Original MOEA/D: Zhang and Li (2007), Sec. V-E, p.721-722
#> 2 original2 | Original MOEA/D, v2: Zhang and Li (2007), Sec. V-F, p.724
#> 3 moead.de | MOEA/D-DE: Li and Zhang (2009)
#>
#>
#> Use preset_moead("name") to generate a standard MOEAD composition
#>
if (FALSE) {
library(smoof) # < Install package smoof if needed
ZDT1 <- make_vectorized_smoof(prob.name = "ZDT1",
dimensions = 30)
problem <- list(name = "ZDT1",
xmin = rep(0, 30),
xmax = rep(1, 30),
m = 2)
# Get preset configuration for original MOEA/D
configuration <- preset_moead("original")
# Modify whatever you fancy:
stopcrit <- list(list(name = "maxiter", maxiter = 50))
showpars <- list(show.iters = "dots", showevery = 10)
seed <- 42
output <- moead(problem = problem,
preset = configuration,
showpars = showpars,
stopcrit = stopcrit,
seed = seed)
}