Problem#

class pybrops.opt.prob.Problem.Problem(n_var=-1, n_obj=1, n_ieq_constr=0, n_eq_constr=0, xl=None, xu=None, vtype=None, vars=None, elementwise=False, elementwise_func=<class 'pymoo.core.problem.ElementwiseEvaluationFunction'>, elementwise_runner=<pymoo.core.problem.LoopedElementwiseEvaluation object>, requires_kwargs=False, replace_nan_values_by=None, exclude_from_serialization=None, callback=None, strict=True, **kwargs)[source]#

Bases: Problem

A semi-abstract base class for representing all optimization problems. This basal semi-abstract class extends the PyMOO Problem class.

The general formulation for an optimization problem should be:

\[\min_{\mathbf{x}} \mathbf{w_F \odot F(x)}\]

Such that:

\[ \begin{align}\begin{aligned}\mathbf{w_G \odot G(x) \leq 0}\\\mathbf{w_H \odot H(x) = 0}\end{aligned}\end{align} \]

A user must implement the following abstract methods in derivatives:

__init__
evalfn
_evaluate

Notes

It is possible to call the constructor of this semi-abstract from a derived class.

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

`bounds`
`do`
`evalfn`	Evaluate a candidate solution for the given Problem.
`evaluate`
`has_bounds`
`has_constraints`
`ideal_point`
`nadir_point`
`name`
`pareto_front`
`pareto_set`

Attributes

`callback`	A callback function to be called after every evaluation.
`data`	Type of the variable to be evaluated.
`decn_space`	Decision space boundaries.
`decn_space_lower`	Lower boundary of the decision space.
`decn_space_upper`	Upper boundary of the decision space.
`elementwise`	Whether the evaluation function should be run elementwise.
`elementwise_func`	A class that creates the function that evaluates a single individual.
`elementwise_runner`	A function that runs the function that evaluates a single individual.
`eqcv_wt`	Equality constraint violation function weights.
`exclude_from_serialization`	attributes which are excluded from being serialized.
`ineqcv_wt`	Inequality constraint violation function weights.
`n_constr`
`n_eq_constr`	n_eq_constr.
`n_ieq_constr`	Number of inequality constraints.
`n_obj`	Number of objectives.
`n_var`	Number of decision variables.
`ndecn`	Number of decision variables.
`neqcv`	Number of equality constraint violations.
`nineqcv`	Number of inequality constraint violation functions.
`nobj`	Number of objectives.
`obj_wt`	Objective function weights.
`replace_nan_values_by`	replace_nan_values_by.
`strict`	Whether the shapes are checked strictly.
`vars`	Variables provided in their explicit form.
`vtype`	The variable type.
`xl`	Lower boundary of the decision space.
`xu`	Upper boundary of the decision space.

property callback: Callable | None#: A callback function to be called after every evaluation.

property data: dict#: Type of the variable to be evaluated.

property decn_space: ndarray | None#: Decision space boundaries.

property decn_space_lower: ndarray | None#: Lower boundary of the decision space.

property decn_space_upper: ndarray | None#: Upper boundary of the decision space.

property elementwise: bool#: Whether the evaluation function should be run elementwise.

property elementwise_func: type#: A class that creates the function that evaluates a single individual.

property elementwise_runner: Callable#: A function that runs the function that evaluates a single individual.

property eqcv_wt: ndarray#: Equality constraint violation function weights.

abstract evalfn(x, *args, **kwargs)[source]#

Evaluate a candidate solution for the given Problem.

This calculates three vectors which are to be minimized:

\[\mathbf{v_{obj}} = \mathbf{w_{obj} \odot F_{obj}(x)} \ \mathbf{v_{ineqcv}} = \mathbf{w_{ineqcv} \odot G_{ineqcv}(x)} \ \mathbf{v_{eqcv}} = \mathbf{w_{eqcv} \odot H_{eqcv}(x)}\]

Parameters:

x (numpy.ndarray) – A candidate solution vector of shape (ndecn,).
args (tuple) – Additional non-keyword arguments.
kwargs (dict) – Additional keyword arguments.

Returns:

out – A tuple (obj, ineqcv, eqcv).

Where:

obj is a numpy.ndarray of shape (nobj,) that contains
objective function evaluations.
ineqcv is a numpy.ndarray of shape (nineqcv,) that contains
inequality constraint violation values.
eqcv is a numpy.ndarray of shape (neqcv,) that contains
equality constraint violation values.

Return type:

tuple

property exclude_from_serialization: Iterable | None#: attributes which are excluded from being serialized.

property ineqcv_wt: ndarray#: Inequality constraint violation function weights.

property n_eq_constr: Integral#: n_eq_constr.

property n_ieq_constr: Integral#: Number of inequality constraints.

property n_obj: Integral#: Number of objectives.

property n_var: Integral#: Number of decision variables.

property ndecn: Integral#: Number of decision variables.

property neqcv: Integral#: Number of equality constraint violations.

property nineqcv: Integral#: Number of inequality constraint violation functions.

property nobj: Integral#: Number of objectives.

property obj_wt: ndarray#: Objective function weights.

property replace_nan_values_by: Real | None#: replace_nan_values_by.

property strict: bool#: Whether the shapes are checked strictly.

property vars: Container | None#: Variables provided in their explicit form.

property vtype: type | None#: The variable type. So far, just used as a type hint.

property xl: ndarray | None#: Lower boundary of the decision space.

property xu: ndarray | None#: Upper boundary of the decision space.