plot_caramel: Plotting of caRamel results

In caRamel: Automatic Calibration by Evolutionary Multi Objective Algorithm

Plotting of caRamel results

Description

Plot graphs of the Pareto front and a graph of optimization evolution

Usage

plot_caramel(caramel_results, nobj = NULL, objnames = NULL)

Arguments

caramel_results	: list resulting from the caRamel() function, with fields $objectives and $save_crit
nobj	: number of objectives (optional)
objnames	: vector of objectives names (optional)

Examples

# Definition of the test function
viennet <- function(i) {
  val1 <- 0.5*(x[i,1]*x[i,1]+x[i,2]*x[i,2])+sin(x[i,1]*x[i,1]+x[i,2]*x[i,2])
  val2 <- 15+(x[i,1]-x[i,2]+1)*(x[i,1]-x[i,2]+1)/27+(3*x[i,1]-2*x[i,2]+4)*(3*x[i,1]-2*x[i,2]+4)/8
  val3 <- 1/(x[i,1]*x[i,1]+x[i,2]*x[i,2]+1) -1.1*exp(-(x[i,1]*x[i,1]+x[i,2]*x[i,2]))
  return(c(val1,val2,val3))
}
nobj <- 3 # Number of objectives
nvar <- 2 # Number of variables
minmax <- c(FALSE, FALSE, FALSE) # All the objectives are to be minimized
bounds <- matrix(data = 1, nrow = nvar, ncol = 2) # Define the bound constraints
bounds[, 1] <- -3 * bounds[, 1]
bounds[, 2] <- 3 * bounds[, 2]

# Caramel optimization
results <- caRamel(nobj, nvar, minmax, bounds, viennet, popsize = 100, archsize = 100,
          maxrun = 500, prec = matrix(1.e-3, nrow = 1, ncol = nobj), carallel = FALSE)

# Plot of results
plot_caramel(results)

caRamel documentation built on March 18, 2022, 7:23 p.m.