windfarmGA (version 5.0.0)

init_population: Create a random initial Population

Description

Create n_start random sub-selections from the indexed grid and assign binary variable 1 to selected grids. This function initiates the genetic algorithm with a first random population and will only be needed in the first iteration.

Usage

init_population(grid, n, n_start = 100)

Value

Returns a list of n_start initial individuals, each consisting of n turbines. Resulting list has the x and y coordinates, the grid cell ID and a binary variable of 1, indicating a turbine in the grid cell.

Arguments

grid

Indexed grid from grid_area() (X, Y, cell IDs).

n

A numeric value indicating the amount of required turbines.

n_start

A numeric indicating the amount of randomly generated initial individuals. Default is 100.

See Also

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

Examples

library(sf)
## Exemplary input Polygon with 2km x 2km:
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
))

Grid <- grid_area(area, 200, 1, TRUE)

## Create 5 individuals with 10 wind turbines each.
firstPop <- init_population(grid = Grid[[1]], n = 10, n_start = 5)