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Multi-Objective Bayesian Optimization via ParEGO

Source: R/bayesopt_parego.R
mlr_loop_functions_parego.Rd

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
)

Arguments

instance

(bbotk::OptimInstanceMultiCrit)
The bbotk::OptimInstanceMultiCrit 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::Archive 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.

Value

invisible(instance)

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

Note

  • The acq_function$surrogate, even if already populated, will always be overwritten by the surrogate.

  • The acq_optimizer$acq_function, even if already populated, will always be overwritten by acq_function.

  • The surrogate$archive, even if already populated, will always be overwritten by the bbotk::Archive of the bbotk::OptimInstanceMultiCrit.

  • The scalarizations of the objective function values are stored as the y_scal column in the bbotk::Archive of the bbotk::OptimInstanceMultiCrit.

  • To make use of parallel evaluations in the case of `q > 1, the objective function of the bbotk::OptimInstanceMultiCrit must be implemented accordingly.

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 = OptimInstanceMultiCrit$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)
}
#> WARN  [16:09:14.656] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this
#> WARN  [16:09:14.696] [bbotk] Could not update the surrogate a final time after the optimization process has terminated.
#>           x  x_domain       y1        y2
#>       <num>    <list>    <num>     <num>
#> 1: 1.134356 <list[1]> 1.286764 0.7493391
#> 2: 1.467836 <list[1]> 2.154544 0.2831980
# }