Swap mutation of turbine layouts

Replace occupied grid cells with unused ones. The number of swaps is max(min_swaps, Binomial(n, p)), so each individual explores at least min_swaps new cells (default 1). The number of turbines stays n.

Usage

swap_mutation(ids, grid_ids, p, seed = NULL, min_swaps = NULL, visit = NULL)

Arguments

ids

Integer matrix with n rows (turbines) and one column per individual

grid_ids

All valid grid cell IDs

p

Mutation probability per turbine

seed

Set a seed for comparability. Default is NULL

min_swaps

Minimum number of swaps per individual. Default is getOption("windfarmGA.min_swaps") (1)

visit

Named visit counts per grid ID. Free cells with fewer visits are more likely to be chosen. Default is NULL (uniform)

Value

Integer matrix of unique grid IDs, same dimension as ids

See also

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

Examples

ids <- cbind(c(1, 3, 5, 7), c(2, 4, 6, 8))
swap_mutation(ids, grid_ids = 1:20, p = 0.5, seed = 1)
#>      [,1] [,2]
#> [1,]    1   19
#> [2,]    3   15
#> [3,]    5    3
#> [4,]   11    8