PSOAlgorithm: PSOAlgorithm
In PFIM: Population Fisher Information Matrix

PSOAlgorithm

R Documentation

PSOAlgorithm

Description

The class PSOAlgorithm implements the PSO algorithm.

Usage

PSOAlgorithm(
  name = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelParameters = list(),
  modelError = list(),
  optimizer = character(0),
  optimizerParameters = list(),
  outputs = list(),
  designs = list(),
  fimType = character(0),
  fim = Fim(),
  odeSolverParameters = list(),
  optimisationDesign = list(),
  optimisationAlgorithmOutputs = list(),
  maxIteration = numeric(0),
  populationSize = numeric(0),
  seed = numeric(0),
  personalLearningCoefficient = numeric(0),
  globalLearningCoefficient = numeric(0),
  showProcess = FALSE
)

Arguments

`name`	A string giving the name of the design evaluation.
`modelEquations`	A list giving the model equations.
`modelFromLibrary`	A list giving the model equations from the library of model.
`modelParameters`	A list giving the model parameters.
`modelError`	A list giving the model error.
`optimizer`	A string giving the name of the optimization algorithm being used.
`optimizerParameters`	A list giving the parameters of the optimization algorithm.
`outputs`	A list giving the model outputs.
`designs`	A list giving the designs to be evaluated.
`fimType`	A string giving the type of Fim being evaluated.
`fim`	A object `Fim` giving the Fim.
`odeSolverParameters`	A list giving the atol and rtol parameters for the ode solver.
`optimisationDesign`	A list giving the evaluation of initial and optimal design.
`optimisationAlgorithmOutputs`	A list giving the outputs of the optimization process.
`maxIteration`	A numeric giving the maxIteration.
`populationSize`	A numeric giving the populationSize.
`seed`	A numeric giving the seed.
`personalLearningCoefficient`	A numeric giving the personalLearningCoefficient.
`globalLearningCoefficient`	A numeric giving the globalLearningCoefficient.
`showProcess`	A Boolean giving the showProcess.