iwopy.interfaces.pymoo.problem.SingleObjProblem¶
- class iwopy.interfaces.pymoo.problem.SingleObjProblem[source]¶
Bases:
Problem
Wrapper around the pymoo problem for a single objective.
At the moment this interface only supports pure int or pure float problems (not mixed).
- Parameters:
problem (iwopy.core.Problem) – The iwopy problem to solve
vectorize (bool, optional) – Switch for vectorized calculations, wrt population individuals
- problem¶
The iwopy problem to solve
- Type:
iwopy.core.Problem
- __init__(problem, vectorize)[source]¶
- Parameters:
n_var (int) – Number of Variables
n_obj (int) – Number of Objectives
n_ieq_constr (int) – Number of Inequality Constraints
n_eq_constr (int) – Number of Equality Constraints
xl (np.array, float, int) – Lower bounds for the variables. if integer all lower bounds are equal.
xu (np.array, float, int) – Upper bounds for the variable. if integer all upper bounds are equal.
vtype (type) – The variable type. So far, just used as a type hint.
Methods
__init__
(problem, vectorize)- param n_var:
Number of Variables
bounds
()do
(X, return_values_of, *args, **kwargs)evaluate
(X, *args[, return_values_of, ...])finalize
(pymoo_results[, verbosity])Finalize the problem.
has_bounds
()has_constraints
()ideal_point
(*args[, use_cache, set_cache])nadir_point
(*args[, use_cache, set_cache])name
()pareto_front
(*args[, use_cache, set_cache])pareto_set
(*args[, use_cache, set_cache])Attributes
n_constr
- __init__(problem, vectorize)[source]¶
- Parameters:
n_var (int) – Number of Variables
n_obj (int) – Number of Objectives
n_ieq_constr (int) – Number of Inequality Constraints
n_eq_constr (int) – Number of Equality Constraints
xl (np.array, float, int) – Lower bounds for the variables. if integer all lower bounds are equal.
xu (np.array, float, int) – Upper bounds for the variable. if integer all upper bounds are equal.
vtype (type) – The variable type. So far, just used as a type hint.