mlr3mbo (version 0.2.9)

mlr_loop_functions_parego: Multi-Objective Bayesian Optimization via ParEGO

Description

Loop function for multi-objective Bayesian Optimization via ParEGO. Normally used inside an OptimizerMbo.

In each iteration after the initial design, the observed objective function values are normalized and q candidates are obtained by scalarizing these values via the augmented Tchebycheff function, updating the surrogate with respect to these scalarized values and optimizing the acquisition function.

Usage

bayesopt_parego(
  instance,
  surrogate,
  acq_function,
  acq_optimizer,
  init_design_size = NULL,
  q = 1L,
  s = 100L,
  rho = 0.05,
  random_interleave_iter = 0L
)

Value

invisible(instance)

The original instance is modified in-place and returned invisible.

Arguments

instance

(bbotk::OptimInstanceBatchMultiCrit)
The bbotk::OptimInstanceBatchMultiCrit to be optimized.

surrogate

(SurrogateLearner)
SurrogateLearner to be used as a surrogate.

acq_function

(AcqFunction)
AcqFunction to be used as acquisition function.

acq_optimizer

(AcqOptimizer)
AcqOptimizer to be used as acquisition function optimizer.

init_design_size

(NULL | integer(1))
Size of the initial design. If NULL and the bbotk::ArchiveBatch contains no evaluations, 4 * d is used with d being the dimensionality of the search space. Points are generated via a Sobol sequence.

q

(integer(1))
Batch size, i.e., the number of candidates to be obtained for a single batch. Default is 1.

s

(integer(1))
$s$ in Equation 1 in Knowles (2006). Determines the total number of possible random weight vectors. Default is 100.

rho

(numeric(1))
$\rho$ in Equation 2 in Knowles (2006) scaling the linear part of the augmented Tchebycheff function. Default is 0.05

random_interleave_iter

(integer(1))
Every random_interleave_iter iteration (starting after the initial design), a point is sampled uniformly at random and evaluated (instead of a model based proposal). For example, if random_interleave_iter = 2, random interleaving is performed in the second, fourth, sixth, ... iteration. Default is 0, i.e., no random interleaving is performed at all.

References

• Knowles, Joshua (2006). “ParEGO: A Hybrid Algorithm With On-Line Landscape Approximation for Expensive Multiobjective Optimization Problems.” IEEE Transactions on Evolutionary Computation, 10(1), 50--66.

See Also

Other Loop Function: loop_function, mlr_loop_functions, mlr_loop_functions_ego, mlr_loop_functions_emo, mlr_loop_functions_mpcl, mlr_loop_functions_smsego

Examples

# \donttest{
if (requireNamespace("mlr3learners") &
    requireNamespace("DiceKriging") &
    requireNamespace("rgenoud")) {

  library(bbotk)
  library(paradox)
  library(mlr3learners)

  fun = function(xs) {
    list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2)
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
  objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)

  instance = OptimInstanceBatchMultiCrit$new(
    objective = objective,
    terminator = trm("evals", n_evals = 5))

  surrogate = default_surrogate(instance, n_learner = 1)

  acq_function = acqf("ei")

  acq_optimizer = acqo(
    optimizer = opt("random_search", batch_size = 100),
    terminator = trm("evals", n_evals = 100))

  optimizer = opt("mbo",
    loop_function = bayesopt_parego,
    surrogate = surrogate,
    acq_function = acq_function,
    acq_optimizer = acq_optimizer)

  optimizer$optimize(instance)
}
# }