gemseo_pymoo / algos / opt

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lib_pymoo module

Pymoo optimization library wrapper.

class gemseo_pymoo.algos.opt.lib_pymoo.PymooAlgorithmDescription(algorithm_name, internal_algorithm_name, library_name='pymoo', description='', website='', handle_integer_variables=True, require_gradient=False, handle_equality_constraints=False, handle_inequality_constraints=True, handle_multiobjective=False, positive_constraints=False, problem_type=ProblemType.NON_LINEAR, require_grad=False)[source]

Bases: OptimizationAlgorithmDescription

The description of an optimization algorithm from the pymoo library.

Parameters:
  • algorithm_name (str)

  • internal_algorithm_name (str)

  • library_name (str) –

    By default it is set to “pymoo”.

  • description (str) –

    By default it is set to “”.

  • website (str) –

    By default it is set to “”.

  • handle_integer_variables (bool) –

    By default it is set to True.

  • require_gradient (bool) –

    By default it is set to False.

  • handle_equality_constraints (bool) –

    By default it is set to False.

  • handle_inequality_constraints (bool) –

    By default it is set to True.

  • handle_multiobjective (bool) –

    By default it is set to False.

  • positive_constraints (bool) –

    By default it is set to False.

  • problem_type (ProblemType) –

    By default it is set to “non-linear”.

  • require_grad (bool) –

    By default it is set to False.

algorithm_name: str

The name of the algorithm in GEMSEO.

handle_equality_constraints: bool = False

Whether the optimization algorithm handles equality constraints.

handle_inequality_constraints: bool = True

Whether the optimization algorithm handles inequality constraints.

handle_integer_variables: bool = True

Whether the optimization algorithm handles integer variables.

internal_algorithm_name: str

The name of the algorithm in the wrapped library.

library_name: str = 'pymoo'

The name of the wrapped library.

positive_constraints: bool = False

Whether the optimization algorithm requires positive constraints.

problem_type: ProblemType = 'non-linear'

The type of problem (see OptimizationProblem.ProblemType).

require_grad: bool = False
class gemseo_pymoo.algos.opt.lib_pymoo.PymooOpt[source]

Bases: OptimizationLibrary

Pymoo optimization library interface.

See gemseo.algos.opt.optimization_library.OptimizationLibrary.

Notes

The missing current values of the DesignSpace attached to the OptimizationProblem are automatically initialized with the method DesignSpace.initialize_missing_current_values().

Constructor.

Generate the library dict, which contains the list of algorithms with their characteristics:

  • does it require gradient

  • does it handle equality constraints

  • does it handle inequality constraints

get_optimum_from_database(message=None, status=None)[source]

Retrieve the optimum from the database.

Override the super class method in order to return an OptimizationResult instance adapted for multi-objective results (see MultiObjectiveOptimizationResult).

Parameters:
  • message (str | None) – The message from the optimizer.

  • status (int | None) – The status from the optimizer.

Returns:

An optimization result object based on the optimum found.

Return type:

OptimizationResult | MultiObjectiveOptimizationResult

CROSSOVER_OPERATOR: Final[str] = 'crossover'

The crossover operator’s name.

EVOLUTIONARY_OPERATORS: Final[dict[str, Operator]] = {'crossover': pymoo.core.crossover.Crossover, 'mutation': pymoo.core.mutation.Mutation, 'sampling': pymoo.core.sampling.Sampling, 'selection': pymoo.core.selection.Selection}

A dictionary with all evolutionary operators available as keys and their Pymoo classes as values.

HV_TOL_ABS: Final[str] = 'hv_tol_abs'

The tag for the absolute tolerance used in the hypervolume convergence check.

HV_TOL_REL: Final[str] = 'hv_tol_rel'

The tag for the relative tolerance used in the hypervolume convergence check.

LIBRARY_NAME: Final[str] = 'pymoo'

The library’s name.

MAX_GEN: Final[str] = 'max_gen'

The tag for the maximum number of generations allowed.

MUTATION_OPERATOR: Final[str] = 'mutation'

The mutation operator’s name.

N_PROCESSES: Final[str] = 'n_processes'

The tag for the number of processes to use.

PYMOO_GA: Final[str] = 'PYMOO_GA'

The GEMSEO alias for the Genetic Algorithm.

PYMOO_NSGA2: Final[str] = 'PYMOO_NSGA2'

The GEMSEO alias for the Non-dominated Sorting Genetic Algorithm II.

PYMOO_NSGA3: Final[str] = 'PYMOO_NSGA3'

The GEMSEO alias for the Non-dominated Sorting Genetic Algorithm III.

PYMOO_RNSGA3: Final[str] = 'PYMOO_RNSGA3'

The GEMSEO alias for the Reference Point Based NSGA-III.

PYMOO_UNSGA3: Final[str] = 'PYMOO_UNSGA3'

The GEMSEO alias for the Unified NSGA-III.

SAMPLING_OPERATOR: Final[str] = 'sampling'

The sampling operator’s name.

SELECTION_OPERATOR: Final[str] = 'selection'

The selection operator’s name.

STOP_CRIT_N_HV: Final[str] = 'stop_crit_n_hv'

The tag for the number of generations to account for in the hypervolume check.

algo_name: str | None

The name of the algorithm used currently.

descriptions: dict[str, AlgorithmDescription]

The description of the algorithms contained in the library.

internal_algo_name: str | None

The internal name of the algorithm used currently.

It typically corresponds to the name of the algorithm in the wrapped library if any.

option_grammar: JSONGrammar | None

The grammar defining the options of the current algorithm.

problem: OptimizationProblem

The optimization problem the driver library is bonded to.

pymoo_n_gen: int = 10000000

The pymoo’s termination criterion based on the number of generations.