optimMaOEA: Elitist Non-dominated Sorting Genetic Algorithm version III
In MaOEA: Many Objective Evolutionary Algorithm

Description Usage Arguments Value Examples

Main interface for the many-objective optimization evolutionary algorithm (MaOEA) package.

optimMaOEA(
  x = NULL,
  fun,
  solver = NSGA3,
  nObjective,
  nGeneration = 1,
  nVar = nrow(x),
  populationSize = ncol(x),
  seed = 2000,
  control = list(),
  ...
)

`x`	The initial population. If not supplied, will be generated using LHS. Column major, each column contain one entry.
`fun`	Objective function being solved.
`solver`	Function name of the solver. Currently available: SMSEMOA, MOCMAES, SMOCMAES, and NSGA3.
`nObjective`	The number of objective functions. A scalar value.
`nGeneration`	Optional, the number of generation the solver should run.
`nVar`	Number of variables, will be used if `x` is not given.
`populationSize`	Number of individuals in the population, will be used if `x` is not given.
`seed`	random number seed for reproduction of code
`control`	A list, containing the following: `weightVectorSet` A set of weight vector for the optimizer. The weight vector can be any point in the objective space. If not supplied, 5*nObjective points are generated from a sobol sequence. Size: nrow = nObjective,ncol = number of weight vectors `crossoverProbability` The probability of doing crossover. Should be between 0-1. Negative value will behave like a zero, and values larger than 1 will behave like 1. Default to 1. `mutationProbability` The probability of doing mutation. Should be between 0-1. Negative value will behave like a zero, and values larger than 1 will behave like 1. Default to 1 `WFGScaling` The use of scaling factor in WFG. Will be ignored in DTLZ problems. Without the scaling, the Pareto front would be on the all-positive portion of hypersphere with radius 1. `mutationDistribution` The distribution index for polynomial mutation. Larger index makes the distribution sharper around the parent. `crossoverDistribution` The distribution index for SBX. Larger index makes the distribution sharper around each parent.
`...`	Further arguments to be passed to `fun`

Returns a list for the next generation population The new generation design points. populationObjective The new generation's objective values.

nVar <- 14
nObjective <- 5
nIndividual <- 100
#control for NSGA3
ctrl <- list(crossoverProbability = 1,
             mutationProbability = 1/nVar)
#Initial population can be supplied, like below but for this example, we skip it
#population <- matrix(runif(nIndividual*nVar), nrow = nVar)

numpyready <- reticulate::py_module_available('numpy')
pygmoready <- reticulate::py_module_available('pygmo')
py_module_ready <- numpyready && pygmoready
if(py_module_ready){ # prevent error on testing the example
# Hybrid NSGA-III and SMSEMOA example
# 2 calls for nObjective. 1 for optimMaOEA, 1 for WFG8
# generate initial population and run 10 gen. NSGA-III with standard WFG8 test function.
newPop <- optimMaOEA( , WFG8,NSGA3,nObjective,10,nVar,nIndividual,,ctrl,nObjective)$x

# run 5 generations of SMSEMOA with standard WFG8 test function starting with newPop.
result <- optimMaOEA( newPop, WFG8,SMSEMOA,nObjective,5,,,1000,ctrl,nObjective)
finalPop <- result$x
finalObjective <- result$y
}