Evaluate the Individual Fitness values

The fitness of all individuals in the current population is calculated after their energy output has been evaluated in calculate_energy. This function reduces the resulting energy outputs to a single fitness value for each individual.

Usage

fitness(
  population,
  reference_height,
  rotor_height,
  surface_roughness,
  area,
  rotor,
  wind,
  elevation = NULL,
  terrain = FALSE,
  ccl_raster = NULL,
  weibull = FALSE,
  parallel = FALSE,
  n_cluster = 2
)

Arguments

population

A list of individuals (layouts with X/Y and cell IDs).

reference_height

Height at which wind$ws was measured.

rotor_height

Hub height in metres.

surface_roughness

Roughness length in metres. Per-cell when terrain is on.

area

Site polygon (sf, SpatialPolygons, or coordinate matrix). Must be projected in metres.

rotor

Rotor radius in metres.

wind

Wind data as returned by windata_format() (list(df, probab)).

elevation

Terrain list from terrain_model() (elevation, orography, roughness). Unused when terrain is FALSE.

terrain

Terrain model (elevation + land cover). TRUE downloads a DEM via elevatr. Pass a DEM raster to skip the download. Per-cell values are computed once and stored in the result as terrainModel for plot_result() / random_search().

ccl_raster

Land-cover roughness raster from terrain_model().

weibull

Raster of estimated wind speeds, or FALSE.

parallel

Parallel fitness (parallel + doParallel).

n_cluster

Worker count when parallel is TRUE.

Value

Returns a list with every individual, consisting of X & Y coordinates, rotor radii, the runs and the selected grid cell IDs, and the resulting energy outputs, efficiency rates and fitness values.

See also

Other Genetic Algorithm Functions: crossover(), genetic_algorithm(), init_population(), mutation(), selection(), set_crossover(), swap_mutation(), trimton()

Examples

# \donttest{
## Create a random rectangular shapefile
library(sf)
area <- sf::st_as_sf(sf::st_sfc(
  sf::st_polygon(list(cbind(
    c(4498482, 4498482, 4499991, 4499991, 4498482),
    c(2668272, 2669343, 2669343, 2668272, 2668272)
  ))),
  crs = 3035
))

## Create a uniform and unidirectional wind data.frame and plots the
## resulting wind rose
## Uniform wind speed and single wind direction
wind <- data.frame(ws = 12, wd = 0)
# windrosePlot <- plot_windrose(data = wind, spd = wind$ws,
#                dir = wind$wd, dirres=10, spdmax=20)

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

wind <- list(wind, probab = 100)
startsel <- init_population(Grid, 10, 20)
fit <- fitness(
  population = startsel, reference_height = 100, rotor_height = 100,
  surface_roughness = 0.3, area = area, rotor = 20,
  wind = wind, terrain = FALSE, parallel = FALSE
)
# }