An objective is used to specify the overall goal of a conservation planning problem. All conservation planning problems involve minimizing or maximizing some kind of objective. For instance, the planner may require a solution that conserves enough habitat for each species while minimizing the overall cost of the reserve network. Alternatively, the planner may require a solution that maximizes the number of conserved species while ensuring that the cost of the reserve network does not exceed the budget.

**Please note that all conservation
planning problems formulated using the prioritizr package require an
objective function, and attempting to solve a problem without an objective
will result in an error.**

The following objectives can be added to a conservation planning
`problem()`

:

`add_min_set_objective()`

Minimize the cost of the solution whilst ensuring that all targets are met. This objective is similar to that used in

*Marxan*.`add_max_cover_objective()`

Represent at least one instance of as many features as possible within a given budget.

`add_max_features_objective()`

Fulfill as many targets as possible while ensuring that the cost of the solution does not exceed a budget.

`add_min_shortfall_objective()`

Minimize the overall (weighted sum) shortfall for as many targets as possible while ensuring that the cost of the solution does not exceed a budget.

`add_min_largest_shortfall_objective()`

Minimize the largest (maximum) shortfall among all targets while ensuring that the cost of the solution does not exceed a budget.

`add_max_phylo_div_objective()`

Maximize the phylogenetic diversity of the features represented in the solution subject to a budget.

`add_max_phylo_end_objective()`

Maximize the phylogenetic endemism of the features represented in the solution subject to a budget.

`add_max_utility_objective()`

Secure as much of the features as possible without exceeding a budget.

Other overviews:
`constraints`

,
`decisions`

,
`importance`

,
`penalties`

,
`portfolios`

,
`solvers`

,
`summaries`

,
`targets`

```
# \dontrun{
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
sim_phylogeny <- get_sim_phylogeny()
# create base problem
p <-
problem(sim_pu_raster, sim_features) %>%
add_relative_targets(0.1) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create problem with added minimum set objective
p1 <- p %>% add_min_set_objective()
# create problem with added maximum coverage objective
# note that this objective does not use targets
p2 <- p %>% add_max_cover_objective(500)
# create problem with added maximum feature representation objective
p3 <- p %>% add_max_features_objective(1900)
# create problem with added minimum shortfall objective
p4 <- p %>% add_min_shortfall_objective(1900)
# create problem with added minimum largest shortfall objective
p5 <- p %>% add_min_largest_shortfall_objective(1900)
# create problem with added maximum phylogenetic diversity objective
p6 <- p %>% add_max_phylo_div_objective(1900, sim_phylogeny)
# create problem with added maximum phylogenetic diversity objective
p7 <- p %>% add_max_phylo_end_objective(1900, sim_phylogeny)
# create problem with added maximum utility objective
# note that this objective does not use targets
p8 <- p %>% add_max_utility_objective(1900)
# solve problems
s <- c(
solve(p1), solve(p2), solve(p3), solve(p4), solve(p5), solve(p6),
solve(p7), solve(p8)
)
#> Warning: Targets specified for the problem will be ignored.
#> ℹ If the targets are important, use a different objective.
#> Warning: Targets specified for the problem will be ignored.
#> ℹ If the targets are important, use a different objective.
names(s) <- c(
"min set", "max coverage", "max features", "min shortfall",
"min largest shortfall", "max phylogenetic diversity",
"max phylogenetic endemism", "max utility"
)
# plot solutions
plot(s, axes = FALSE)
# }
```