Pagmo¶
- class Pagmo(*args)¶
Pagmo algorithms.
This class exposes bio-inspired and evolutionary global optimization algorithms from the Pagmo library. These algorithms start from an initial population and make it evolve to obtain a final population after a defined number of generations (by
setMaximumIterationNumber()
). A few of these algorithms allow for multi-objective optimization, and in that case the result is not the best point among the final population but a set of dominant points: a pareto front.- Parameters:
- problem
OptimizationProblem
Optimization problem to solve
- algoNamestr, default=’gaco’
Identifier of the optimization method to use.
- startingSample2-d sequence of float, optional
Initial population
- problem
Notes
The total number of evaluations is the size of the initial population multiplied by the iteration number plus one. Starting points provided through the startingSample parameter should be within the bounds of the
OptimizationProblem
, but this is not enforced.Pagmo provides the following global heuristics:
Algorithm
Description
Multi-objective
MINLP
Batch
gaco
Extended Ant Colony Optimization
no
yes
yes
de
Differential Evolution
no
no
no
sade
Self-adaptive DE (jDE and iDE)
no
no
no
de1220
Self-adaptive DE (de_1220 aka pDE)
no
no
no
gwo
Grey wolf optimizer
no
no
no
ihs
Improved Harmony Search
no
yes
no
pso
Particle Swarm Optimization
no
no
no
pso_gen
Particle Swarm Optimization Generational
no
no
yes
sea
(N+1)-ES Simple Evolutionary Algorithm
no
no
no
sga
Simple Genetic Algorithm
no
yes
no
simulated_annealing
Corana’s Simulated Annealing
no
no
no
bee_colony
Artificial Bee Colony
no
no
no
cmaes
Covariance Matrix Adaptation Evo. Strategy
no
no
yes
xnes
Exponential Evolution Strategies
no
no
no
nsga2
Non-dominated Sorting GA
yes
yes
yes
moead
Multi-objective EA with Decomposition
yes
no
no
moead_gen
Multi-objective EA with Decomposition Gen.
yes
no
yes
mhaco
Multi-objective Hypervolume-based ACO
yes
yes
yes
nspso
Non-dominated Sorting PSO
yes
no
yes
Only gaco and ihs natively support constraints, but for the other algorithms constraints are emulated through penalization. For mhaco, the initial population must satisfy constraints, else it is built by boostrap on valid points with the same population size as the one provided. Some algorithms support batch evaluation, see
setBlockSize()
. Default parameters are available in theResourceMap
for each algorithm, refer to the correspondings keys in the Pagmo documentation.Examples
Define an optimization problem to find the minimum of the Rosenbrock function:
>>> import openturns as ot >>> dim = 2 >>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2']) >>> bounds = ot.Interval([-5.0] * dim, [5.0] * dim) >>> problem = ot.OptimizationProblem(rosenbrock) >>> problem.setBounds(bounds)
Sample the initial population inside a box:
>>> uniform = ot.JointDistribution([ot.Uniform(-2.0, 2.0)] * dim) >>> ot.RandomGenerator.SetSeed(0) >>> init_pop = uniform.getSample(5)
Run GACO on our problem:
>>> algo = ot.Pagmo(problem, 'gaco', init_pop) >>> algo.setMaximumIterationNumber(5) >>> algo.run() >>> result = algo.getResult() >>> x_star = result.getOptimalPoint() >>> y_star = result.getOptimalValue()
Get the final population:
>>> final_pop_x = result.getFinalPoints() >>> final_pop_y = result.getFinalValues()
Define a multi-objective problem:
>>> dim = 2 >>> model = ot.SymbolicFunction(['x', 'y'], ['x^2+y^2*(1-x)^3', '-x^2']) >>> bounds = ot.Interval([-2.0] * dim, [3.0] * dim) >>> problem = ot.OptimizationProblem(model) >>> problem.setBounds(bounds)
Sample the initial population inside a box:
>>> uniform = ot.JointDistribution([ot.Uniform(-2.0, 3.0)] * dim) >>> ot.RandomGenerator.SetSeed(0) >>> init_pop = uniform.getSample(5)
Run NSGA2 on our problem:
>>> algo = ot.Pagmo(problem, 'nsga2', init_pop) >>> algo.setMaximumIterationNumber(5) >>> algo.run() >>> result = algo.getResult() >>> final_pop_x = result.getFinalPoints() >>> final_pop_y = result.getFinalValues()
Get the best front points and values:
>>> front0 = result.getParetoFrontsIndices()[0] >>> front0_x = final_pop_x.select(front0) >>> front0_y = final_pop_y.select(front0)
Methods
Accessor to the list of algorithm names provided.
Accessor to the algorithm name.
Block size accessor.
Accessor to check status flag.
Accessor to the object's name.
Accessor to maximum allowed absolute error.
Accessor to maximum allowed number of calls.
Accessor to maximum allowed constraint error.
Accessor to maximum allowed number of iterations.
Accessor to maximum allowed relative error.
Accessor to maximum allowed residual error.
Accessor to the maximum duration.
getName
()Accessor to the object's name.
Accessor to optimization problem.
Accessor to optimization result.
getSeed
()Random generator seed accessor.
Accessor to starting point.
Accessor to the sample of starting points.
hasName
()Test if the object is named.
run
()Launch the optimization.
setAlgorithmName
(algoName)Accessor to the algorithm name.
setBlockSize
(blockSize)Block size accessor.
setCheckStatus
(checkStatus)Accessor to check status flag.
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.
setMaximumIterationNumber
(maximumIterationNumber)Accessor to maximum allowed number of iterations.
setMaximumRelativeError
(maximumRelativeError)Accessor to maximum allowed relative error.
setMaximumResidualError
(maximumResidualError)Accessor to maximum allowed residual error.
setMaximumTimeDuration
(maximumTime)Accessor to the maximum duration.
setName
(name)Accessor to the object's name.
setProblem
(problem)Accessor to optimization problem.
setProgressCallback
(*args)Set up a progress callback.
setResult
(result)Accessor to optimization result.
setSeed
(seed)Random generator seed accessor.
setStartingPoint
(point)Accessor to starting point.
setStartingSample
(startingSample)Accessor to the sample of starting points.
setStopCallback
(*args)Set up a stop callback.
getMaximumEvaluationNumber
setMaximumEvaluationNumber
- __init__(*args)¶
- static GetAlgorithmNames()¶
Accessor to the list of algorithm names provided.
- Returns:
- names
Description
List of algorithm names provided, according to its naming convention.
- names
- getAlgorithmName()¶
Accessor to the algorithm name.
- Returns:
- algoNamestr
The identifier of the algorithm.
- getBlockSize()¶
Block size accessor.
- Returns:
- blockSizeint
Batch evaluation granularity.
- getCheckStatus()¶
Accessor to check status flag.
- Returns:
- checkStatusbool
Whether to check the termination status. If set to False,
run()
will not throw an exception if the algorithm does not fully converge and will allow one to still find a feasible candidate.
- getClassName()¶
Accessor to the object’s name.
- Returns:
- class_namestr
The object class name (object.__class__.__name__).
- getMaximumAbsoluteError()¶
Accessor to maximum allowed absolute error.
- Returns:
- maximumAbsoluteErrorfloat
Maximum allowed absolute error, where the absolute error is defined by where and are two consecutive approximations of the optimum.
- getMaximumCallsNumber()¶
Accessor to maximum allowed number of calls.
- Returns:
- maximumEvaluationNumberint
Maximum allowed number of direct objective function calls through the () operator. Does not take into account eventual indirect calls through finite difference gradient calls.
- getMaximumConstraintError()¶
Accessor to maximum allowed constraint error.
- Returns:
- maximumConstraintErrorfloat
Maximum allowed constraint error, where the constraint error is defined by where is the current approximation of the optimum and is the function that gathers all the equality and inequality constraints (violated values only)
- getMaximumIterationNumber()¶
Accessor to maximum allowed number of iterations.
- Returns:
- maximumIterationNumberint
Maximum allowed number of iterations.
- getMaximumRelativeError()¶
Accessor to maximum allowed relative error.
- Returns:
- maximumRelativeErrorfloat
Maximum allowed relative error, where the relative error is defined by if , else .
- getMaximumResidualError()¶
Accessor to maximum allowed residual error.
- Returns:
- maximumResidualErrorfloat
Maximum allowed residual error, where the residual error is defined by if , else .
- getMaximumTimeDuration()¶
Accessor to the maximum duration.
- Returns:
- maximumTimefloat
Maximum optimization duration in seconds.
- getName()¶
Accessor to the object’s name.
- Returns:
- namestr
The name of the object.
- getProblem()¶
Accessor to optimization problem.
- Returns:
- problem
OptimizationProblem
Optimization problem.
- problem
- getResult()¶
Accessor to optimization result.
- Returns:
- result
OptimizationResult
Result class.
- result
- getSeed()¶
Random generator seed accessor.
- Returns:
- seedint
Seed.
- getStartingSample()¶
Accessor to the sample of starting points.
- Returns:
- startingSample
Sample
The initial population.
- startingSample
- hasName()¶
Test if the object is named.
- Returns:
- hasNamebool
True if the name is not empty.
- run()¶
Launch the optimization.
- setAlgorithmName(algoName)¶
Accessor to the algorithm name.
- Parameters:
- algoNamestr
The identifier of the algorithm.
- setBlockSize(blockSize)¶
Block size accessor.
- Parameters:
- blockSizeint
Batch evaluation granularity.
- setCheckStatus(checkStatus)¶
Accessor to check status flag.
- Parameters:
- checkStatusbool
Whether to check the termination status. If set to False,
run()
will not throw an exception if the algorithm does not fully converge and will allow one to still find a feasible candidate.
- setMaximumAbsoluteError(maximumAbsoluteError)¶
Accessor to maximum allowed absolute error.
- Parameters:
- maximumAbsoluteErrorfloat
Maximum allowed absolute error, where the absolute error is defined by where and are two consecutive approximations of the optimum.
- setMaximumCallsNumber(maximumCallsNumber)¶
Accessor to maximum allowed number of calls
- Parameters:
- maximumEvaluationNumberint
Maximum allowed number of direct objective function calls through the () operator. Does not take into account eventual indirect calls through finite difference gradient calls.
- setMaximumConstraintError(maximumConstraintError)¶
Accessor to maximum allowed constraint error.
- Parameters:
- maximumConstraintErrorfloat
Maximum allowed constraint error, where the constraint error is defined by where is the current approximation of the optimum and is the function that gathers all the equality and inequality constraints (violated values only)
- setMaximumIterationNumber(maximumIterationNumber)¶
Accessor to maximum allowed number of iterations.
- Parameters:
- maximumIterationNumberint
Maximum allowed number of iterations.
- setMaximumRelativeError(maximumRelativeError)¶
Accessor to maximum allowed relative error.
- Parameters:
- maximumRelativeErrorfloat
Maximum allowed relative error, where the relative error is defined by if , else .
- setMaximumResidualError(maximumResidualError)¶
Accessor to maximum allowed residual error.
- Parameters:
- maximumResidualErrorfloat
Maximum allowed residual error, where the residual error is defined by if , else .
- setMaximumTimeDuration(maximumTime)¶
Accessor to the maximum duration.
- Parameters:
- maximumTimefloat
Maximum optimization duration in seconds.
- setName(name)¶
Accessor to the object’s name.
- Parameters:
- namestr
The name of the object.
- setProblem(problem)¶
Accessor to optimization problem.
- Parameters:
- problem
OptimizationProblem
Optimization problem.
- problem
- setProgressCallback(*args)¶
Set up a progress callback.
Can be used to programmatically report the progress of an optimization.
- Parameters:
- callbackcallable
Takes a float as argument as percentage of progress.
Examples
>>> import sys >>> import openturns as ot >>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2']) >>> problem = ot.OptimizationProblem(rosenbrock) >>> solver = ot.OptimizationAlgorithm(problem) >>> solver.setStartingPoint([0, 0]) >>> solver.setMaximumResidualError(1.e-3) >>> solver.setMaximumCallsNumber(10000) >>> def report_progress(progress): ... sys.stderr.write('-- progress=' + str(progress) + '%\n') >>> solver.setProgressCallback(report_progress) >>> solver.run()
- setResult(result)¶
Accessor to optimization result.
- Parameters:
- result
OptimizationResult
Result class.
- result
- setSeed(seed)¶
Random generator seed accessor.
- Parameters:
- seedint
Seed.
Notes
The default is set by the Pagmo-InitialSeed ResourceMap entry.
- setStartingSample(startingSample)¶
Accessor to the sample of starting points.
- Parameters:
- startingSample2-d sequence of float
The initial population.
- setStopCallback(*args)¶
Set up a stop callback.
Can be used to programmatically stop an optimization.
- Parameters:
- callbackcallable
Returns an int deciding whether to stop or continue.
Examples
>>> import openturns as ot >>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2']) >>> problem = ot.OptimizationProblem(rosenbrock) >>> solver = ot.OptimizationAlgorithm(problem) >>> solver.setStartingPoint([0, 0]) >>> solver.setMaximumResidualError(1.e-3) >>> solver.setMaximumCallsNumber(10000) >>> def ask_stop(): ... return True >>> solver.setStopCallback(ask_stop) >>> solver.run()
Examples using the class¶
Multi-objective optimization using Pagmo