# Number of dimensions of the optimisation problem (for all the examples)
D <- 5
# Boundaries of the search space (Rastrigin function)
lower <- rep(-5.12, D)
upper <- rep(5.12, D)
# Setting the home directory of the user as working directory
setwd("~")
################################
# Example 1. Basic use #
################################
# Setting the seed (for reproducible results)
set.seed(100)
# Basic use 1. Rastrigin function (non-linear and multi-modal with many local minima)
# Results are not saved to the hard disk, for faster execution ('write2disk=FALSE')
hydroPSO(fn=rastrigin, lower=lower, upper=upper, control=list(write2disk=FALSE) )
# Basic use 2. Rastrigin function (non-linear and multimodal with many local minima)
# Results are saved to the hard disk. Slower than before but results are kept for
# future inspection
hydroPSO(fn=rastrigin, lower=lower, upper=upper )
# Plotting the results, by default into the active graphic device
# 'MinMax="min"' indicates a minimisation problem
plot_results(MinMax="min")
# Plotting the results into PNG files.
plot_results(MinMax="min", do.png=TRUE)
################################
# Example 2. More advanced use #
################################
# Defining the relative tolerance ('reltol'), the frequency of report messages
# printed to the screen ('REPORT'), and no output files ('write2disk')
set.seed(100)
hydroPSO( fn=rastrigin, lower=lower, upper=upper,
control=list(reltol=1e-20, REPORT=10, write2disk=FALSE) )
###################################
# Example 3. von Neumman Topology #
###################################
# Same as Example 2, but using a von Neumann topology ('topology="vonNeumann"')
set.seed(100)
hydroPSO(fn=rastrigin,lower=lower,upper=upper,
control=list(topology="vonNeumann", reltol=1E-20,
REPORT=50, write2disk=FALSE) )
################################
# Example 4. Regrouping #
################################
# Same as Example 3 ('topology="vonNeumann"') but using regrouping ('use.RG')
set.seed(100)
hydroPSO(fn=rastrigin,lower=lower,upper=upper,
control=list(topology="vonNeumann", reltol=1E-20,
REPORT=50, write2disk=FALSE,
use.RG=TRUE,RG.thr=7e-2,RG.r=3,RG.miniter=50) )
################################
# Example 5. FIPS #
################################
# Same as Example 3 ('topology="vonNeumann"') but using a fully informed
# particle swarm (FIPS) variant ('method') with global best topology
set.seed(100)
hydroPSO(fn=rastrigin,lower=lower,upper=upper, method="fips",
control=list(topology="gbest",reltol=1E-9,write2disk=FALSE) )
################################
# Example 6. normalisation #
################################
# Same as Example 3 but parameter values are normalised to the [0,1] interval
# during the optimisation. This option is recommended when the search space is
# not an hypercube (not useful is this particular example)
set.seed(100)
hydroPSO(fn=rastrigin,lower=lower,upper=upper,
control=list(topology="vonNeumann", reltol=1E-20, normalise=TRUE,
REPORT=50, write2disk=FALSE) )
################################
# Example 7. Asynchronus update#
################################
# Same as Example 3, but using asynchronus update of previus and local best
# ('best.update'). Same global optimum but much slower....
set.seed(100)
hydroPSO(fn=rastrigin,lower=lower,upper=upper,
control=list(topology="vonNeumann", reltol=1E-20,
REPORT=50, write2disk=FALSE, best.update="async") ) # dontrun ENDRun the code above in your browser using DataLab