RealProblem#

class pybrops.opt.prob.RealProblem.RealProblem(ndecn, decn_space, decn_space_lower, decn_space_upper, nobj, obj_wt=None, nineqcv=None, ineqcv_wt=None, neqcv=None, eqcv_wt=None, vtype=None, vars=None, elementwise=True, elementwise_func=<class 'pymoo.core.problem.ElementwiseEvaluationFunction'>, elementwise_runner=<pymoo.core.problem.LoopedElementwiseEvaluation object>, replace_nan_values_by=None, exclude_from_serialization=None, callback=None, strict=True, **kwargs)[source]#

Bases: Problem

Partially implemented class for optimization problems with nominal decision variables where the goal is to select an optimal subset.

Constructor for RealProblem.

Parameters:
  • ndecn (Integral) – Number of decision variables.

  • decn_space (numpy.ndarray) – A 1d array containing the set of available elements

  • decn_space_lower (numpy.ndarray) – A 1d array representing the lower bound of the decision variables.

  • decn_space_upper (numpy.ndarray) – A 1d array representing the upper bound of the decision variables.

  • nobj (Integral) – Number of objectives for the problem.

  • obj_wt (numpy.ndarray, Real, None) –

    Objective function weights. Weights from this vector are applied to objective function values via the Hadamard product. If values are 1.0 or -1.0, this can be used to specify minimizing and maximizing objectives, respectively.

    If obj_wt is numpy.ndarray, then the array must be of shape (nobj,).

    If obj_wt is Real, then the value is broadcast to a numpy.ndarray of shape (nobj,).

    If obj_wt is None, then the value 1.0 is broadcast to a numpy.ndarray of shape (nobj,). This assumes that all objectives are to be minimized.

  • nineqcv (Integral, None) –

    Number of inequality constraint violation functions. This is equivalent to the vector length returned by the ineqcv_trans function. Must be Integral greater than or equal to zero.

    If nineqcv is None, then set to zero.

  • ineqcv_wt (numpy.ndarray, None) –

    Inequality constraint violation function weights. Weights from this vector are applied to inequality constraint violation function values via the Hadamard product. If values are 1.0 or -1.0, this can be used to specify minimizing and maximizing constraints, respectively.

    If ineqcv_wt is numpy.ndarray, then the array must be of shape (nineqcv,).

    If ineqcv_wt is Real, then the value is broadcast to a numpy.ndarray of shape (nineqcv,).

    If ineqcv_wt is None, then the value 1.0 is broadcast to a numpy.ndarray of shape (nineqcv,). This assumes that all constraints are to be minimized.

  • neqcv (Integral, None) –

    Number of equality constraint violations. This is equivalent to the vector length returned by the eqcv_trans function. Must be Integral greater than or equal to zero.

    If neqcv is None, then set to zero.

  • eqcv_wt (numpy.ndarray, None) –

    Equality constraint violation function weights. Weights from this vector are applied to equality constraint violation function values via the Hadamard product. If values are 1.0 or -1.0, this can be used to specify minimizing and maximizing constraints, respectively.

    If eqcv_wt is numpy.ndarray, then the array must be of shape (neqcv,).

    If eqcv_wt is Real, then the value is broadcast to a numpy.ndarray of shape (neqcv,).

    If eqcv_wt is None, then the value 1.0 is broadcast to a numpy.ndarray of shape (neqcv,). This assumes that all constraints are to be minimized.

  • vtype (type, None) – Used by PyMOO interface to construct a PyMOO Problem object.

  • vars (Sequence, None) – Used by PyMOO interface to construct a PyMOO Problem object.

  • elementwise (bool) – Used by PyMOO interface to construct a PyMOO Problem object.

  • elementwise_func (type) – Used by PyMOO interface to construct a PyMOO Problem object.

  • elementwise_runner (Callable) – Used by PyMOO interface to construct a PyMOO Problem object.

  • replace_nan_values_by (Real, None) – Used by PyMOO interface to construct a PyMOO Problem object.

  • exclude_from_serialization (Iterable, None) – Used by PyMOO interface to construct a PyMOO Problem object.

  • callback (Callable, None) – Used by PyMOO interface to construct a PyMOO Problem object.

  • strict (bool) – Used by PyMOO interface to construct a PyMOO Problem object.

  • kwargs (dict) – Additional keyword arguments used for cooperative inheritance.

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)#

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.