Adjust the amount of turbines per windfarm

Adjust the mutated individuals to the required amount of turbines.

Usage

trimton(mut, nturb, allparks, nGrids, trimForce, seed)

Arguments

mut

A binary matrix with the mutated individuals

nturb

A numeric value indicating the amount of required turbines

allparks

A data.frame consisting of all individuals of the current generation

nGrids

A numeric value indicating the total amount of grid cells

trimForce

If TRUE the algorithm will use a probabilistic approach to correct the windfarms to the desired amount of turbines. If FALSE the adjustment will be random. Default is FALSE

seed

Set a seed for comparability. Default is NULL

Value

Returns a binary matrix with the correct amount of turbines per individual

See also

Other Genetic Algorithm Functions: crossover(), fitness(), genetic_algorithm(), init_population(), mutation(), selection(), windfarmGA()

Examples

# \donttest{
## Create a random rectangular shapefile
library(sf)
Polygon1 <- sf::st_as_sf(sf::st_sfc(
  sf::st_polygon(list(cbind(
    c(0, 0, 2000, 2000, 0),
    c(0, 2000, 2000, 0, 0)))),
  crs = 3035
))

## Create a uniform and unidirectional wind data.frame and plots the
## resulting wind rose
## Uniform wind speed and single wind direction
data.in <- as.data.frame(cbind(ws=12, wd=0))

## Calculate a Grid and an indexed data.frame with coordinates and grid cell Ids.
Grid1 <- grid_area(shape = Polygon1, size = 200, prop = 1);
Grid <- Grid1[[1]]
AmountGrids <- nrow(Grid)

startsel <- init_population(Grid,10,20);
wind <- as.data.frame(cbind(ws=12,wd=0))
wind <- list(wind, probab = 100)
fit <- fitness(selection = startsel, referenceHeight = 100, RotorHeight = 100,
              SurfaceRoughness=0.3, Polygon = Polygon1, resol1 = 200, rot = 20, 
              dirspeed = wind, srtm_crop="", topograp=FALSE, cclRaster="")
allparks <- do.call("rbind", fit);

## SELECTION
## print the amount of Individuals selected.
## Check if the amount of Turbines is as requested.
selec6best <- selection(fit, Grid,2, TRUE, 6, "VAR");
selec6best <- selection(fit, Grid,2, TRUE, 6, "FIX");
selec6best <- selection(fit, Grid,4, FALSE, 6, "FIX");

## CROSSOVER
## u determines the amount of crossover points,
## crossPart determines the method used (Equal/Random),
## uplimit is the maximum allowed permutations
crossOut <- crossover(selec6best, 2, uplimit = 300, crossPart="RAN");
crossOut <- crossover(selec6best, 7, uplimit = 500, crossPart="RAN");
crossOut <- crossover(selec6best, 3, uplimit = 300, crossPart="EQU");

## MUTATION
## Variable Mutation Rate is activated if more than 2 individuals represent
## the current best solution.
mut <- mutation(a = crossOut, p = 0.3, NULL);

## TRIMTON
## After Crossover and Mutation, the amount of turbines in a windpark change and have to be
## corrected to the required amount of turbines.
mut1 <- trimton(mut = mut, nturb = 10, allparks = allparks, nGrids = AmountGrids,
                trimForce = FALSE)
colSums(mut)
#>  [1] 33 34 29 40 38 35 29 26 28 34 32 36 42 38 35 34 35 41 38 30 38 34 35 35 41
#> [26] 39 37 36 33 39 38 34 26 26 41 39 37 34 34 40 39 35 37 33 39 37 49 35 28 34
#> [51] 35 32 33 43 40 28 33 35 43 29 34 34 41 28
colSums(mut1)
#>  [1] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
#> [26] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
#> [51] 10 10 10 10 10 10 10 10 10 10 10 10 10 10

# }