fitness: Evaluate the Individual Fitness values

In windfarmGA: Genetic Algorithm for Wind Farm Layout Optimization

Evaluate the Individual Fitness values

Description

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

## 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
)

windfarmGA documentation built on Sept. 14, 2026, 9:08 a.m.