The blackbox optimization benchmarking-constrained (bbob-constrained) test suite

Abstract

This package provides a wrapper of the COCO experiments libarary’s bbob-constrained test suite.

APIs

class Problem(function_id: int, dimension: int, instance_id: int = 1)

• function_id: ID of the bbob-constrained benchmark function to use. It must be in the range of [1, 54].
• dimension: Dimension of the benchmark function. It must be in [2, 3, 5, 10, 20, 40].
• instance_id: ID of the instance of the benchmark function. It must be in the range of [1, 15].

Methods and Properties

• search_space: Return the search space.
  • Returns: dict[str, optuna.distributions.BaseDistribution]
• directions: Return the optimization directions.
  • Returns: list[optuna.study.StudyDirection]
• __call__(trial: optuna.Trial): Evaluate the objective function and return the objective value.
  • Args:
    • trial: Optuna trial object.
  • Returns: float
• evaluate(params: dict[str, float]): Evaluate the objective function and return the objective value.
  • Args:
    • params: Decision variable like {"x0": x1_value, "x1": x1_value, ..., "xn": xn_value}. The number of parameters must be equal to dimension.
  • Returns: float
• constraints_func(trial: optuna.Trial.FrozenTrial): Evaluate the constraint functions and return the list of constraint functions values.
  • Args:
    • trial: Optuna trial object.
  • Returns: list[float]
• evaluate_constraints(params: dict[str, float]): Evaluate the constraint functions and return the list of constraint functions values.
  • Args:
    • params: Decision variable like {"x0": x1_value, "x1": x1_value, ..., "xn": xn_value}. The number of parameters must be equal to dimension.
  • Returns: list[float]

The properties defined by cocoex.Problem are also available such as number_of_objectives.

Installation

Please install the coco-experiment package.

pip install -U coco-experiment

Example

import optuna
import optunahub


bbob_constrained = optunahub.load_module("benchmarks/bbob_constrained")
constrained_sphere2d = bbob_constrained.Problem(function_id=1, dimension=2, instance_id=1)

study = optuna.create_study(
    sampler=optuna.samplers.TPESampler(
        constraints_func=constrained_sphere2d.constraints_func
    ),
    directions=constrained_sphere2d.directions
)
study.optimize(constrained_sphere2d, n_trials=20)

try:
    print(study.best_trial.params, study.best_trial.value)
except Exception as e:
    print(e)

Details of Benchmark Functions

Please refer to the paper for details about each benchmark function.

Reference

Paul Dufossé, Nikolaus Hansen, Dimo Brockhoff, Phillipe R. Sampaio, Asma Atamna, and Anne Auger. Building scalable test problems for benchmarking constrained optimizers. 2022. To be submitted to the SIAM Journal of Optimization.