metaheuristic_designer.constraint_handlers.linear_bound_penalty_constraint module

class LinearBoundPenaltyConstraint(dimension, alpha=1, lower_bound=-100, upper_bound=100, **kwargs)

Bases: PenalizeConstraint

Encodes a bound constraint by adding a penalty proportional to the distance of the solution’s distance to the bounds.

Parameters:

dimension: int

size of the input vector (decoded).

alpha: float, optional

factor to multiply to the penalty before being applied.

lower_bound: float | ndarray, optional

lower limit of the bounds.

upper_bound: float | ndarray, optional

upper limit of the bounds.

Attributes:

params

Access parameter values by attribute-style lookup.

Parameters:

• alpha (ScalarLike)

• lower_bound (ScalarLike | VectorLike)

• upper_bound (ScalarLike | VectorLike)

Methods

get_params()	Return a copy of the current parameter dictionary.
penalty(population_matrix)	Offset to the objective value for the solution corresponding to violations of the problem's constraints.
repair_solutions(solution)	Modifies the incoming solution so that it follows the problem's constraints.
store_kwargs([progress])	Store keyword arguments and evaluate them at the given progress.
update(progress)	Re-evaluate all stored parameters at the current progress.
update_kwargs([progress])	Add or replace parameters and immediately evaluate them.

get_state
repair_population

penalty(population_matrix)

Offset to the objective value for the solution corresponding to violations of the problem’s constraints.

Return type:

ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool]

Parameters:

solution: Any

The input solution.

Returns:

penalty: float

The amount of penalty to apply to the current solution.

Parameters:

population_matrix (ndarray[tuple[int, int], floating] | ndarray[tuple[int, int], integer] | ndarray[tuple[int, int], uint8 | bool])