BBOB

class ioh.iohcpp.problem.BBOB

Bases: RealSingleObjective

Black-Box Optimization Benchmarking (BBOB) problem set.

Contains 24 noiselessreal-valued test functions supported on [-5, 5]^n, where n is the dimensionality.

This problem was orginally proposed by Hansen et. al. in [FinckHRA10] and was implemented as the core component of the COmparing Continous Optimizer (COCO) platform [HansenARMTB20].

We took the implementation of those 24 functions in https://github.com/numbbo/coco/tree/master/code-experiments/src (v2.2) and adopted those to our framework.

We have acknowledged and specified in our license file https://github.com/IOHprofiler/IOHexperimenter/blob/master/LICENSE.md the usage and modification to the COCO/BBOB sources.

Reference

[HansenARMTB20] Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tusar, and Dimo Brockhoff. “COCO: A platform for comparing continuous optimizers in a black-box setting.” Optimization Methods and Software (2020): 1-31.

[FinckHRA10] Steffen Finck, Nikolaus Hansen, Raymond Ros, and Anne Auger. “Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions.” Technical Report 2009/20, Research Center PPE, 2009. Updated February, 2010.

Attributes Summary

bounds	The bounds of the problem.
constraints	The constraints of the problem.
create
log_info	The data is that being sent to the logger.
meta_data	The static meta-data of the problem containing, e.g., problem id, instance id, and problem's dimensionality
optimum	The optimum and its objective value for a problem instance
problems	All registered problems
state	The current state of the optimization process containing, e.g., the current solution and the number of function evaluated consumed so far

Methods Summary

__call__([order, device, writable])	Overloaded function.
add_constraint(self, arg, /)	add a constraint
attach_logger(self, arg, /)	Attach a logger to the problem to allow performance tracking.
detach_logger(self)	Remove the specified logger from the problem.
enforce_bounds(self[, weight, how, exponent])	Enforced the bounds (box-constraints) as constraint :param weight: :type weight: The weight for computing the penalty (can be infinity to have strict box-constraints) :param how: :type how: The enforcement strategy, should be one of the 'ioh.ConstraintEnforcement' options :param exponent: :type exponent: The exponent for scaling the contraint
invert(self)
remove_constraint(self, arg, /)	remove a constraint
reset(self)	Reset all state variables of the problem.
set_final_target(self, new_target)	update the final target
set_id(self, new_problem_id)	update the problem id
set_instance(self, new_instance)	update the problem instance
set_name(self, new_name)	update the problem name

Attributes Documentation

bounds

The bounds of the problem.

constraints

The constraints of the problem.

create = <nanobind.nb_func object>

log_info

The data is that being sent to the logger.

meta_data

The static meta-data of the problem containing, e.g., problem id, instance id, and problem’s dimensionality

optimum

The optimum and its objective value for a problem instance

problems

All registered problems

state

The current state of the optimization process containing, e.g., the current solution and the number of function evaluated consumed so far

Methods Documentation

__call__(self, arg: ndarray[dtype=float64, shape=(*), order='C', device='cpu', writable=False], /) → float

__call__(self, arg: collections.abc.Sequence[float], /) → float

__call__(self, arg: ndarray[dtype=float64, shape=(*, *), order='C', device='cpu', writable=False], /) → numpy.ndarray[dtype=float64, shape=(*)]

__call__(self, arg: collections.abc.Sequence[collections.abc.Sequence[float]], /) → numpy.ndarray[dtype=float64, shape=(*)]

Overloaded function.

1. __call__(self, arg: ndarray[dtype=float64, shape=(*), order='C', device='cpu', writable=False], /) -> float

2. __call__(self, arg: collections.abc.Sequence[float], /) -> float

   Evaluate the problem.

   x: list

   the search point to evaluate. It must be a 1-dimensional array/list whose length matches search space’s dimensionality

   float

   The evaluated search point

3. __call__(self, arg: ndarray[dtype=float64, shape=(*, *), order='C', device='cpu', writable=False], /) -> numpy.ndarray[dtype=float64, shape=(*)]

4. __call__(self, arg: collections.abc.Sequence[collections.abc.Sequence[float]], /) -> numpy.ndarray[dtype=float64, shape=(*)]

   Evaluate the problem at several search points.

   x: list[list]

   the search points to evaluate, one per row. It must be a 2-dimensional array/list whose rows’ length matches search space’s dimensionality

   numpy.ndarray

   The objective values of the search points: a new float64 array with one value per row

add_constraint(self, arg: ioh.iohcpp.AbstractRealConstraint, /) → None

add a constraint

attach_logger(self, arg: ioh.iohcpp.logger.Logger, /) → None

Attach a logger to the problem to allow performance tracking.

Parameters:

logger (Logger) – A logger-object from the IOHexperimenter logger module.

detach_logger(self) → None

Remove the specified logger from the problem.

enforce_bounds(self, weight: float = 1.0, how: ioh.iohcpp.ConstraintEnforcement = ConstraintEnforcement.SOFT, exponent: float = 1.0) → None

Enforced the bounds (box-constraints) as constraint :param weight: :type weight: The weight for computing the penalty (can be infinity to have strict box-constraints) :param how: :type how: The enforcement strategy, should be one of the ‘ioh.ConstraintEnforcement’ options :param exponent: :type exponent: The exponent for scaling the contraint

invert(self) → None

remove_constraint(self, arg: ioh.iohcpp.AbstractRealConstraint, /) → None

remove a constraint

reset(self) → None

Reset all state variables of the problem.

set_final_target(self, new_target: float) → None

update the final target

set_id(self, new_problem_id: int) → None

update the problem id

set_instance(self, new_instance: int) → None

update the problem instance

set_name(self, new_name: str) → None

update the problem name