windfarmGA (version 2.3.0)

trimton: Adjust the amount of turbines per windfarm

Description

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

A boolean value which determines which adjustment method should be used. TRUE uses a probabilistic approach and FALSE uses a random approach

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

# NOT RUN {
## Create a random rectangular shapefile
library(sp)
Polygon1 <- Polygon(rbind(c(0, 0), c(0, 2000), c(2000, 2000), c(2000, 0)))
Polygon1 <- Polygons(list(Polygon1),1);
Polygon1 <- SpatialPolygons(list(Polygon1))
Projection <- "+init=epsg:3035"
proj4string(Polygon1) <- CRS(Projection)

## 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,resol = 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)
colSums(mut1)

# }