OBCDO

Module: opytimizer.optimizers.single_objective.science.cdo

Opposition-Based Chernobyl Disaster Optimizer.

This variant implements multiple Opposition-Based Learning strategies:

• Basic OBL (BOBL)
• Quasi OBL (QOBL)
• Generalized OBL (GOBL)
• Partial OBL (POBL)
• Center-Based OBL (COBL)
• Enhanced OBL (EOBL)
• Time-Varying OBL (TVOBL)
• Elite OBL (Elite-OBL)

Constructor

OBCDO(params: Optional[Dict[str, Any]] = None) -> None

Parameters

Parameter	Type	Default	Description
params	Optional[Dict[str, Any]]	None	—

Methods

adapt_obl_rate

adapt_obl_rate(self, success_rate: float) -> None

Adapt opposition rate based on success rate.

Parameters

Parameter	Type	Default	Description
success_rate	float		Rate of successful oppositions

get_center_opposite_position

get_center_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace) -> numpy.ndarray

Center-Based Opposition-Based Learning (COBL).

Returns: Center-based opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds

get_elite_opposite_position

get_elite_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace) -> numpy.ndarray

Elite Opposition-Based Learning (Elite-OBL).

Returns: Elite-based opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds

get_enhanced_opposite_position

get_enhanced_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace) -> numpy.ndarray

Enhanced Opposition-Based Learning (EOBL).

Returns: Enhanced opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds

get_generalized_opposite_position

get_generalized_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace) -> numpy.ndarray

Generalized Opposition-Based Learning (GOBL).

Returns: Generalized opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds

get_opposite_position

get_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace) -> numpy.ndarray

Basic Opposition-Based Learning (BOBL).

Returns: Opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds

get_partial_opposite_position

get_partial_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace) -> numpy.ndarray

Partial Opposition-Based Learning (POBL).

Returns: Partial opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds

get_quasi_opposite_position

get_quasi_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace) -> numpy.ndarray

Quasi Opposition-Based Learning (QOBL).

Returns: Quasi-opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds

get_time_varying_opposite_position

get_time_varying_opposite_position(self, position: numpy.ndarray, space: opytimizer.core.space._SingleObjectiveSpace, iteration: int, n_iterations: int) -> numpy.ndarray

Time-Varying Opposition-Based Learning (TVOBL).

Returns: Time-varying opposite position

Parameters

Parameter	Type	Default	Description
position	numpy.ndarray		Current position
space	opytimizer.core.space._SingleObjectiveSpace		Space object containing bounds
iteration	int		Current iteration
n_iterations	int		Maximum iterations

select_strategy

select_strategy(self, iteration: int, n_iterations: int) -> str

Select OBL strategy based on current state.

Returns: Selected strategy name

Parameters

Parameter	Type	Default	Description
iteration	int		Current iteration
n_iterations	int		Maximum iterations

update

update(self, space: opytimizer.core.space._SingleObjectiveSpace, function: opytimizer.core.function.Function, iteration: int, n_iterations: int) -> None

Updates using Opposition-Based Learning.

Parameters

Parameter	Type	Default	Description
space	opytimizer.core.space._SingleObjectiveSpace		Space containing agents and update-related information
function	opytimizer.core.function.Function		Objective function
iteration	int		Current iteration
n_iterations	int		Maximum iterations

update_elite_solutions

update_elite_solutions(self, space: opytimizer.core.space._SingleObjectiveSpace, function: opytimizer.core.function.Function) -> None

Update elite solutions pool.

Parameters

Parameter	Type	Default	Description
space	opytimizer.core.space._SingleObjectiveSpace		—
function	opytimizer.core.function.Function		—