R/mc-prioritize.r
This function is a thin wrapper around optim() that performs conservation
prioritization using simulated annealing.
mc_prioritize(objective, nsf, x, n = 10000L, itemp = 10, n_temp = 10L, maximize = TRUE, verbose = TRUE)
| objective | an objective function to optimize over, typically created by
|
|---|---|
| nsf | a neighbour selection function, typically created by
|
| x | logical vector specifying which planning units are selected as a
starting point. Traditional conservation prioritization methods, such as
those implemented by the |
| n | integer; number of simulated annealing iterations. |
| itemp | numeric; initial temperature for the simulated annealing cooling schedule. Higher temperatures will result in bad changes being more likely to be accepted. |
| n_temp | integer; number of objective function evaluations at each temperature in the annealing process. |
| maximize | logical; whether the objective function is to be maximized or
minimized. The standard objective function generated by
|
| verbose | logical; whether to print the simulated annealing output as the heuristic progresses. |
A logical vector indicating which planning units are selected in the final solution.
# generate features r <- raster::raster(nrows = 10, ncols = 10, crs = "+proj=aea", vals = sample(0:1, 100, replace = TRUE)) s <- raster::stack(r, r, r) s[[2]][] <- sample(0:1, 100, replace = TRUE, prob = c(0.6, 0.4)) s[[3]][] <- sample(0:1, 100, replace = TRUE, prob = c(0.8, 0.2)) names(s) <- c("a", "b", "c") features <- raster::rasterToPolygons(s) features <- sf::st_as_sf(features) # cost features$cost <- runif(nrow(features)) # dispersal functions disp_f <- list(a = dispersal_negexp(1 / 0.01), b = dispersal_negexp(1 / 0.005), c = dispersal_negexp(1 / 0.02)) # calculate scale factors scale_mc <- mc_reserve(features, rep(TRUE, nrow(features)), disp_f) # set budget at 50% of total budget <- 0.5 * sum(features$cost) # build an objective function and neighbour selection function objective <- generate_objective(features, disp_f, budget, delta = 0.001, blm = 0.001, units = "km") nsf <- generate_nsf(features, buffer = 20) # random starting point x_start <- sample(c(FALSE, TRUE), 100, replace = TRUE, prob = c(0.9, 0.1)) # optimize mc_prioritize(objective, nsf, x_start, n = 50L)#> sann objective function values #> initial value 0.000748 #> final value 0.000748 #> sann stopped after 49 iterations#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE #> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [25] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE #> [37] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> [73] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [85] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> [97] FALSE FALSE FALSE FALSE