smsemoa function

Implementation of the SMS-EMOA by Emmerich et al.

Pure R implementation of the SMS-EMOA. This algorithm belongs to the group of indicator based multi-objective evolutionary algorithms. In each generation, the SMS-EMOA selects two parents uniformly at, applies recombination and mutation and finally selects the best subset of individuals among all subsets by maximizing the Hypervolume indicator.

smsemoa(
  fitness.fun,
  n.objectives = NULL,
  n.dim = NULL,
  minimize = NULL,
  lower = NULL,
  upper = NULL,
  mu = 100L,
  ref.point = NULL,
  mutator = setup(mutPolynomial, eta = 25, p = 0.2, lower = lower, upper = upper),
  recombinator = setup(recSBX, eta = 15, p = 0.7, lower = lower, upper = upper),
  terminators = list(stopOnIters(100L)),
  ...
)

Arguments

• fitness.fun: [function]

  The fitness function.

• n.objectives: [integer(1)]

  Number of objectives of obj.fun. Optional if obj.fun is a benchmark function from package smoof.

• n.dim: [integer(1)]

  Dimension of the decision space.

• minimize: [logical(n.objectives)]

  Logical vector with ith entry TRUE if the ith objective of fitness.fun

  shall be minimized. If a single logical is passed, it is assumed to be valid for each objective.

• lower: [numeric]

  Vector of minimal values for each parameter of the decision space in case of float or permutation encoding. Optional if obj.fun is a benchmark function from package smoof.

• upper: [numeric]

  Vector of maximal values for each parameter of the decision space in case of float or permutation encoding. Optional if obj.fun is a benchmark function from package smoof.

• mu: [integer(1)]

  Number of individuals in the population. Default is 100.

• ref.point: [numeric]

  Reference point for the hypervolume computation. Default is (11, ..., 11)' with the corresponding dimension.

• mutator: [ecr_mutator]

  Mutation operator of type ecr_mutator.

• recombinator: [ecr_recombinator]

  Recombination operator of type ecr_recombinator.

• terminators: [list]

  List of stopping conditions of type ecr_terminator . Default is to stop after 100 iterations.

• ...: [any]

  Further arguments passed down to fitness function.

Returns

[ecr_multi_objective_result]

Note

This helper function hides the regular ecr interface and offers a more R like interface of this state of the art EMOA.

References

Beume, N., Naujoks, B., Emmerich, M., SMS-EMOA: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research, Volume 181, Issue 3, 16 September 2007, Pages 1653-1669.