smsemoa: Implementation of the SMS-EMOA by Emmerich et al.

Description

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.

Usage

smsemoa(task, n.population = 100L, ref.point = NULL, parent.selector = setupSimpleSelector(), mutator = setupPolynomialMutator(eta = 25, p = 0.2), recombinator = setupSBXRecombinator(eta = 15, p = 0.7), max.iter = NULL, max.evals = NULL, max.time = NULL, ...)

Arguments

task

[ecr_optimization_task] Optimization task. If a smoof_function is passed it is automatically converted into a task.

n.population

[integer(1)] Population size. Default is 100.

ref.point

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

parent.selector

[ecr_selector] Selection operator which implements a procedure to copy individuals from a given population to the mating pool, i. e., allow them to become parents.

mutator

[ecr_mutator] Mutation operator of type ecr_mutator.

recombinator

[ecr_recombinator] Recombination operator of type ecr_recombinator.

max.iter

[integer(1)] Maximal number of iterations. Default ist 100L.

max.evals

[integer(1)] Maximal number of iterations/generations. Default is Inf.

max.time

[integer(1)] Time budget in seconds. Default ist Inf.

...

[any] Further arguments passed to setupECRControl.

Value

[ecr_smsemoa_result, ecr_multi_objective_result]

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.