metaheuristic_designer.objective_function module

Base class for the Objective Function module.

This module implements the objective function that will measure the quality of the solutions.

class ObjectiveFunc(dimension, lower_bound=None, upper_bound=None, constraint_handler=None, mode='max', name='Some function', vectorized=False, recalculate=False, **kwargs)

Bases: ParametrizableMixin, ABC

Abstract objective function with built-in fitness conversion.

Subclasses must implement objective(), which returns the raw objective value. The base class automatically converts it to a fitness that is always maximized (flipping the sign for minimization) and applies a penalty if a ConstraintHandler is present.

Parameters:

dimensionint

Number of decision variables.

lower_boundfloat or array-like, optional

Lower bound(s) of the feasible region. When both bounds are given, a ClipBoundConstraint is added automatically.

upper_boundfloat or array-like, optional

Upper bound(s) of the feasible region.

constraint_handlerConstraintHandler, optional

Handler that can repair solutions and/or compute penalties.

modestr, optional

"max" or "min". The fitness is always maximized internally; the mode controls the sign conversion.

namestr, optional

Human-readable name for this function.

vectorizedbool, optional

If True, objective() receives the whole population at once and must return an array.

recalculatebool, optional

If True, every individual is re-evaluated even if its fitness has already been computed.

**kwargs

Additional keyword arguments stored as schedulable parameters.

Attributes:

params

Access parameter values by attribute-style lookup.

Parameters:

• dimension (int)

• lower_bound (Optional[ScalarLike | VectorLike])

• upper_bound (Optional[ScalarLike | VectorLike])

• constraint_handler (Optional[ConstraintHandler])

• mode (str)

• name (str)

• vectorized (bool)

• recalculate (bool)

Methods

__call__(population)	Shorthand for executing the objective function on a vector.
add_parameter_constraints(...)	Attach extra constraint handlers for extended encodings (e.g., PSO).
calculate_fitness(population)	Evaluate fitness for the whole population.
get_params()	Return a copy of the current parameter dictionary.
get_state()	Return a dictionary with the current configuration.
objective(solution)	Implementation of the objective function.
repair_population(population)	Transforms an invalid vector into one that satisfies the restrictions of the problem.
restart()	Reset the evaluation counter to zero.
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.

calculate_fitness(population)

Evaluate fitness for the whole population.

The raw objective values are computed via objective(), penalties are subtracted, and the result (always maximized) is stored in population.fitness. Individuals that already have a valid fitness are skipped unless recalculate is set.

Return type:

Population

Parameters:

populationPopulation

The population whose individuals will be evaluated.

parallelbool, optional

Reserved for future use, currently ignored.

threadsint, optional

Reserved for future use, currently ignored.

Returns:

ndarray

The new fitness values (also written in-place).

Parameters:

population (Population)

abstract objective(solution)

Implementation of the objective function.

Return type:

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

Parameters:

solution: Any

The solution for which the fitness will be calculated.

Returns:

objective_value: VectorLike | ScalarLike

Value of the objective function given a solution.

Parameters:

solution (Any)

repair_population(population)

Transforms an invalid vector into one that satisfies the restrictions of the problem.

Return type:

Population

Parameters:

solution: MatrixLike

A solution that could be violating the restrictions of the problem.

Returns:

repaired_solution: MatrixLike

A modified version of the solution passed that satisfies the restrictions of the problem.

Parameters:

population (Population)

add_parameter_constraints(parameter_extending_encoding, param_handlers)

Attach extra constraint handlers for extended encodings (e.g., PSO).

Parameters:

parameter_extending_encodingParameterExtendingEncoding

The encoding that splits the genotype into solution and auxiliary parameters.

param_handlersdict

Mapping from parameter names to ConstraintHandler instances.

Parameters:

• parameter_extending_encoding (ParameterExtendingEncoding)

• param_handlers (dict[str, ConstraintHandler])

restart()

Reset the evaluation counter to zero.

get_state()

Return a dictionary with the current configuration.

Return type:

dict

Returns:

dict

Keys include class_name, name, constraint handler state, and all stored parameters.

class NullObjectiveFunc(**kwargs)

Bases: ObjectiveFunc

Objective function that always returns zero.

Useful as a placeholder in tests or when the optimization criterion is handled entirely by constraints.

Parameters:

**kwargs

Forwarded to ObjectiveFunc.

Attributes:

params

Access parameter values by attribute-style lookup.

Methods

__call__(population)	Shorthand for executing the objective function on a vector.
add_parameter_constraints(...)	Attach extra constraint handlers for extended encodings (e.g., PSO).
calculate_fitness(population)	Evaluate fitness for the whole population.
get_params()	Return a copy of the current parameter dictionary.
get_state()	Return a dictionary with the current configuration.
objective(_)	Implementation of the objective function.
repair_population(population)	Transforms an invalid vector into one that satisfies the restrictions of the problem.
restart()	Reset the evaluation counter to zero.
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.

objective(_)

Implementation of the objective function.

Return type:

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

Parameters:

solution: Any

The solution for which the fitness will be calculated.

Returns:

objective_value: VectorLike | ScalarLike

Value of the objective function given a solution.

class ObjectiveFromLambda(obj_func, dimension, lower_bound=None, upper_bound=None, constraint_handler=None, mode='max', name=None, vectorized=False, recalculate=False, **kwargs)

Bases: ObjectiveFunc

Objective function indicated by a function call.

Parameters:

obj_func: Callable

Objective function as a callable object.

dimension: int

The dimension of the vectors accepted by the objective function.

mode: str, optional

Whether to maximize or minimize the function (using the string ‘max’ or ‘min’).

lower_bound: float, optional

Lower limit restriction for the vectors.

upper_bound: float, optional

Upper limit restriction for the vectors.

name: str, optional

The name that will be displayed to represent this function.

Attributes:

params

Access parameter values by attribute-style lookup.

Parameters:

• obj_func (Callable)

• dimension (int)

• lower_bound (Optional[ScalarLike | VectorLike])

• upper_bound (Optional[ScalarLike | VectorLike])

• constraint_handler (Optional[ConstraintHandler])

• mode (str)

• name (Optional[str])

• vectorized (bool)

• recalculate (bool)

Methods

__call__(population)	Shorthand for executing the objective function on a vector.
add_parameter_constraints(...)	Attach extra constraint handlers for extended encodings (e.g., PSO).
calculate_fitness(population)	Evaluate fitness for the whole population.
get_params()	Return a copy of the current parameter dictionary.
get_state()	Return a dictionary with the current configuration.
objective(solution)	Implementation of the objective function.
repair_population(population)	Transforms an invalid vector into one that satisfies the restrictions of the problem.
restart()	Reset the evaluation counter to zero.
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.

objective(solution)

Implementation of the objective function.

Return type:

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

Parameters:

solution: Any

The solution for which the fitness will be calculated.

Returns:

objective_value: VectorLike | ScalarLike

Value of the objective function given a solution.

Parameters:

solution (Any)