MultiObjectiveOptimizationAnalysis¶

class persalys.MultiObjectiveOptimizationAnalysis(*args)¶

Perform the multi-objective optimization analysis of a model.

Parameters:

namestr: Name
physicalModelPhysicalModel: Physical model
solverNamestr: Optimization algorithm name (optional). Use GetSolverNames() to list available names.

Attributes:

AlgorithmDictionary

Methods

`GetSolverNames`(*args)	Accessor to the list of solver names.
`addConstraint`(*args)	Adds a constraint to the analysis
`getBlockSize`()	Accessor to the block size.
`getBounds`()	Accessor to bounds.
`getClassName`()	Accessor to the object's name.
`getErrorMessage`()	Error message accessor.
`getGenerationNumber`()	Accessor to the number of generations.
`getInterestVariables`()	Get the variables to analyse.
`getMaximumAbsoluteError`()	Accessor to maximum allowed absolute error.
`getMaximumConstraintError`()	Accessor to maximum allowed constraint error.
`getMaximumRelativeError`()	Accessor to maximum allowed relative error.
`getMaximumResidualError`()	Accessor to maximum allowed residual error.
`getMinimization`()	Test whether this is a minimization or maximization problem.
`getName`()	Accessor to the object's name.
`getPhysicalModel`()	Physical model accessor.
`getPopulationSize`()	Accessor to popSize.
`getPythonScript`()	Physical model accessor.
`getRawEquations`()	Accessor to constraints equations
`getResult`()	Accessor to result.
`getSeed`()	Accessor to random generator seed.
`getSolverName`()	Accessor to solver name
`getStartingPoint`()	Accessor to starting point.
`getVariableInputs`()	Accessor to variable input names
`getWarningMessage`()	Warning message accessor.
`hasName`()	Test if the object is named.
`hasValidResult`()	Whether the analysis has been run.
`isReliabilityAnalysis`()	Whether the analysis involves reliability.
`isRunning`()	Whether the analysis is running.
`resetConstraints`()	Clears the current analysis constraints
`run`()	Launch the analysis.
`setBlockSize`(blockSize)	Accessor to the block size.
`setBounds`(bounds)	Accessor to bounds.
`setGenerationNumber`(generationNumber)	Accessor to the number of generations.
`setInterestVariables`(variablesNames)	Set the variables to analyse.
`setMaximumAbsoluteError`(maximumAbsoluteError)	Accessor to maximum allowed absolute error.
`setMaximumCallsNumber`(maximumCallsNumber)	Accessor to maximum allowed number of calls.
`setMaximumConstraintError`(maximumConstraintError)	Accessor to maximum allowed constraint error.
`setMaximumRelativeError`(maximumRelativeError)	Accessor to maximum allowed relative error.
`setMaximumResidualError`(maximumResidualError)	Accessor to maximum allowed residual error.
`setMinimization`(varNames)	Tell whether this is a minimization or maximization problem.
`setName`(name)	Accessor to the object's name.
`setPopulationSize`(popSize)	Accessor to popSize.
`setSeed`(seed)	Accessor to random generator seed.
`setSolverName`(name)	Accessor to solver name
`setStartingPoint`(startingPoint)	Accessor to starting point.
`setVariableInputs`(inputs)	Accessor to variable input names

defineProblem
getEqualityConstraints
getInequalityConstraints
getMaximumCallsNumber
getVariablesType
setVariablesType

Examples

>>> import openturns as ot
>>> import persalys

Create the model:

>>> X0 = persalys.Input('X0', 0)
>>> X1 = persalys.Input('X1', 0)
>>> Y0 = persalys.Output('Y0')
>>> Y1 = persalys.Output('Y1')
>>> bounds = ot.Interval([0.0, 0.0], [5.0, 5.0])
>>> model = persalys.SymbolicPhysicalModel('aModelPhys', [X0, X1], [Y0, Y1], ['X0', 'var g := 1.0 + 9.0 * (X0 + X1); g * (1.0 - sqrt(X0 / g))'])

Create the analysis:

>>> analysis = persalys.MultiObjectiveOptimizationAnalysis('anAnalysis', model, 'nsga2')
>>> analysis.setInterestVariables(['Y0', 'Y1'])
>>> analysis.setBounds(bounds)
>>> analysis.setPopulationSize(50)
>>> analysis.setGenerationNumber(10)
>>> analysis.setVariableInputs(['X0', 'X1'])
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()
>>> finalPop = result.getFinalPop()
>>> fronts = result.getFronts()

__init__(*args)¶

static GetSolverNames(*args)¶

Accessor to the list of solver names.

Returns:

namesDescription: List of available solver names.

addConstraint(*args)¶

Adds a constraint to the analysis

Parameters:

constraintstr: Constraint equation

getBlockSize()¶

Accessor to the block size.

Returns:

blockSizeint: Maximum number of samples queued for evaluation.

getBounds()¶

Accessor to bounds.

Returns:

boundsInterval: Problem bounds.

getClassName()¶

Accessor to the object’s name.

Returns:

class_namestr: The object class name (object.__class__.__name__).

getErrorMessage()¶

Error message accessor.

Returns:

messagestr: Error message if the analysis failed

getGenerationNumber()¶

Accessor to the number of generations.

Returns:

generationNumberint: number of generations for the evolving algorithm.

getInterestVariables()¶

Get the variables to analyse.

Returns:

variablesNamessequence of str: Names of the variables to analyse

getMaximumAbsoluteError()¶

Accessor to maximum allowed absolute error.

Returns:

maximumAbsoluteErrorfloat: Maximum allowed absolute error.

getMaximumConstraintError()¶

Accessor to maximum allowed constraint error.

Returns:

maximumConstraintErrorfloat: Maximum allowed constraint error.

getMaximumRelativeError()¶

Accessor to maximum allowed relative error.

Returns:

maximumRelativeErrorfloat: Maximum allowed relative error.

getMaximumResidualError()¶

Accessor to maximum allowed residual error.

Returns:

maximumResidualErrorfloat: Maximum allowed residual error.

getMinimization()¶

Test whether this is a minimization or maximization problem.

Returns:

valuebool: True if this is a minimization problem (default), False otherwise.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getPhysicalModel()¶

Physical model accessor.

Returns:

modelPhysicalModel: Physical model

getPopulationSize()¶

Accessor to popSize.

Returns:

popSizeint: Initial population sample size.

getPythonScript()¶

Physical model accessor.

Returns:

scriptstr: Python script to replay the analysis

getRawEquations()¶

Accessor to constraints equations

Returns:

equationssequence of str: Constraints equations

getResult()¶

Accessor to result.

Returns:

resultMultiObjectiveOptimizationAnalysisResult: multi-objective analysis result.

getSeed()¶

Accessor to random generator seed.

Returns:

seedint: random generator seed for the evolving algorithm.

getSolverName()¶

Accessor to solver name

Returns:

solverstr: Solver name

getStartingPoint()¶

Accessor to starting point.

Returns:

startingPointPoint: Starting point.

getVariableInputs()¶

Accessor to variable input names

Returns:

inputsDescription: Variable input names. It is set by default to the list of inputs of the model.

getWarningMessage()¶

Warning message accessor.

Returns:

messagestr: Warning message which can appear during the analysis computation

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasValidResult()¶

Whether the analysis has been run.

Returns:

hasValidResultbool: Whether the analysis has already been run

isReliabilityAnalysis()¶

Whether the analysis involves reliability.

Returns:

isReliabilityAnalysisbool: Whether the analysis involves a reliability analysis

isRunning()¶

Whether the analysis is running.

Returns:

isRunningbool: Whether the analysis is running

resetConstraints()¶: Clears the current analysis constraints

run()¶: Launch the analysis.

setBlockSize(blockSize)¶

Accessor to the block size.

Parameters:

blockSizeint: Maximum number of samples queued for evaluation.

setBounds(bounds)¶

Accessor to bounds.

Parameters:

boundsInterval: Problem bounds.

setGenerationNumber(generationNumber)¶

Accessor to the number of generations.

Parameters:

generationNumberint: number of generations for the evolving algorithm.

setInterestVariables(variablesNames)¶

Set the variables to analyse.

Parameters:

variablesNamessequence of str: Names of the variables to analyse

setMaximumAbsoluteError(maximumAbsoluteError)¶

Accessor to maximum allowed absolute error.

Parameters:

maximumAbsoluteErrorfloat: Maximum allowed absolute error.

setMaximumCallsNumber(maximumCallsNumber)¶

Accessor to maximum allowed number of calls.

Parameters:

Nint: Maximum allowed number of calls.

setMaximumConstraintError(maximumConstraintError)¶

Accessor to maximum allowed constraint error.

Parameters:

maximumConstraintErrorfloat: Maximum allowed constraint error.

setMaximumRelativeError(maximumRelativeError)¶

Accessor to maximum allowed relative error.

Parameters:

maximumRelativeErrorfloat: Maximum allowed relative error.

setMaximumResidualError(maximumResidualError)¶

Accessor to maximum allowed residual error.

Parameters:

maximumResidualErrorfloat: Maximum allowed residual error.

setMinimization(varNames)¶

Tell whether this is a minimization or maximization problem.

Parameters:

minimizationbool: True if this is a minimization problem, False otherwise.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setPopulationSize(popSize)¶

Accessor to popSize.

Parameters:

popSizeint: Initial population sample size.

setSeed(seed)¶

Accessor to random generator seed.

Parameters:

seedint: random generator seed for the evolving algorithm.

setSolverName(name)¶

Accessor to solver name

Parameters:

solverstr: Solver name. Use GetSolverNames() to list available names.

setStartingPoint(startingPoint)¶

Accessor to starting point.

Parameters:

startingPointsequence of float: Starting point.

setVariableInputs(inputs)¶

Accessor to variable input names

Parameters:

inputssequence of str: Variable input names. It is set by default to the list of inputs of the model.

MultiObjectiveOptimizationAnalysis¶

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