metaheuristic_designer.constraint_handlers.composite_constraint module#

class CompositeConstraint(constraints, **kwargs)[source]#

Bases: ConstraintHandler

applies every constraint handler in succession.

Parameters:

constraints: Iterable: List of constraint handlers.

Attributes:

params: Access parameter values by attribute-style lookup.

Parameters:

constraints (Iterable)

Methods

`get_params`()	Return a copy of the current parameter dictionary.
`penalty`(solution)	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.

gather_params
get_state
repair_population

gather_params()[source]#

repair_solutions(solution)[source]#

Modifies the incoming solution so that it follows the problem’s constraints.

Return type:

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

Parameters:

solution: Any: The input solution.

Returns:

fixed_solution: Any: Modified version of the input solution that fits the problem’s constraints

Parameters:

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

penalty(solution)[source]#

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

Return type:

number | float | int

Parameters:

solution: Any: The input solution.

Returns:

penalty: float: The amount of penalty to apply to the current solution.

Parameters:

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