Identify the best solver currently installed on the system and specify that it should be used to solve a conservation planning problem(). For information on the performance of different solvers, please see Schuster et al. (2020) for benchmarks comparing the run time and solution quality of some of the available solvers when applied to different sized datasets.

add_default_solver(x, ...)



problem() (i.e., ConservationProblem) object.


arguments passed to the solver.


Object (i.e., ConservationProblem) with the solver added to it.


Ranked from best to worst, the available solvers that can be used are: add_gurobi_solver(), add_cplex_solver(), add_cbc_solver(), add_highs_solver(), add_lpsymphony_solver(), and finally add_rsymphony_solver().


Schuster R, Hanson JO, Strimas-Mackey M, and Bennett JR (2020). Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems. PeerJ, 8: e9258.

See also

See solvers for an overview of all functions for adding a solver.

Other solvers: add_cbc_solver(), add_cplex_solver(), add_gurobi_solver(), add_highs_solver(), add_lsymphony_solver, add_rsymphony_solver()