View source: R/add_random_solver.R
add_random_solver  R Documentation 
Specify that solutions should be generated using random processes. Although prioritizations should be developed using optimization routines, a portfolio of randomly generated solutions can be useful for evaluating the effectiveness of solutions.
add_random_solver(x, number_solutions = 1, verbose = TRUE)
x 
ProjectProblem object. 
number_solutions 

verbose 

The algorithm used to randomly generate solutions depends on the
the objective specified for the project prioritization
problem()
.
For objectives which maximize benefit subject to budgetary constraints
(e.g. add_max_richness_objective()
):
All locked in and zerocost actions are initially selected for funding (excepting actions which are locked out).
A project—and all of its associated actions—is randomly selected for funding (excepting projects associated with locked out actions, and projects which would cause the budget to be exceeded when added to the existing set of selected actions).
The previous step is repeated until no more projects can be selected for funding without the total cost of the prioritized actions exceeding the budget.
For objectives which minimize cost subject to biodiversity constraints
(i.e. add_min_set_objective()
:
All locked in and zerocost actions are initially selected for funding (excepting actions which are locked out).
A project—and all of its associated actions—is randomly selected for funding (excepting projects associated with locked out actions, and projects which would cause the budget to be exceeded when added to the existing set of selected actions).
The previous step is repeated until all of the persistence targets are met.
ProjectProblem object with the solver added to it.
solvers.
# load data data(sim_projects, sim_features, sim_actions) # build problem with random solver, and generate 100 random solutions p1 < problem(sim_projects, sim_actions, sim_features, "name", "success", "name", "cost", "name") %>% add_max_richness_objective(budget = 200) %>% add_binary_decisions() %>% add_random_solver(number_solutions = 100) # print problem print(p1) # solve problem s1 < solve(p1) # print solutions print(s1) # plot first random solution plot(p1, s1) # plot histogram of the objective values for the random solutions hist(s1$obj, xlab = "Expected richness", xlim = c(0, 2.5), main = "Histogram of random solutions") # since the objective values don't tell us much about the quality of the # solutions, we can find the optimal solution and calculate how different # each of the random solutions is from optimality ## Not run: # find the optimal objective value using an exact algorithms solver s2 < p1 %>% add_default_solver() %>% solve() # create new column in s1 with percent difference from optimality s1$optimality_diff < ((s2$obj  s1$obj) / s1$obj) * 100 # plot histogram showing the quality of the random solutions # higher numbers indicate worse solutions hist(s1$optimality_diff, xlab = "Difference from optimality (%)", main = "Histogram of random solutions", xlim = c(0, 50)) ## End(Not run)
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