optim2Opt: Two-Opt

In CEGO: Combinatorial Efficient Global Optimization

Description

Implementation of a Two-Opt local search.

Usage

optim2Opt(x = NULL, fun, control = list())

Arguments

x	start solution of the local search
fun	function that determines cost or length of a route/permutation
control	(list), with the options: archive Whether to keep all candidate solutions and their fitness in an archive (TRUE) or not (FALSE). Default is TRUE. budget The limit on number of target function evaluations (stopping criterion) (default: 100) creationFunction Function to create individuals/solutions in search space. Default is a function that creates random permutations of length 6 vectorized Boolean. Defines whether target function is vectorized (takes a list of solutions as argument) or not (takes single solution as argument). Default: FALSE

Value

a list with:

xbest

best solution found

ybest

fitness of the best solution

count

number of performed target function evaluations

References

Wikipedia contributors. "2-opt." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 13 Jun. 2014. Web. 21 Oct. 2014.

See Also

optimCEGO, optimEA, optimRS, optimMaxMinDist

Examples

seed=0
#distance
dF <- distancePermutationHamming
#creation
cF <- function()sample(5)
#objective function
lF <- landscapeGeneratorUNI(1:5,dF)
#start optimization
set.seed(seed)
res <- optim2Opt(,lF,list(creationFunction=cF,budget=100,
   vectorized=TRUE)) ##target function is "vectorized", expects list of solutions as input
res

CEGO documentation built on May 14, 2021, 1:08 a.m.