This function will not speed up optimization!
optimize.portfolio.parallel(R, portfolio, optimize_method = c("DEoptim",
"random", "ROI", "pso", "GenSA"), search_size = 20000, trace = FALSE, ...,
rp = NULL, momentFUN = "set.portfolio.moments", message = FALSE,
nodes = 4)a list containing the optimal weights, some summary statistics, the function call, and optionally trace information
an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
an object of type "portfolio" specifying the constraints and objectives for the optimization
one of "DEoptim", "random", "pso", "GenSA".
integer, how many portfolios to test, default 20,000
TRUE/FALSE if TRUE will attempt to return additional information on the path or portfolios searched
any other passthru parameters
matrix of random portfolio weights, default NULL, mostly for automated use by rebalancing optimization or repeated tests on same portfolios
the name of a function to call to set portfolio moments, default set.portfolio.moments_v2
TRUE/FALSE. The default is message=FALSE. Display messages if TRUE.
how many processes to run in the foreach loop, default 4
Kris Boudt, Peter Carl, Brian G. Peterson
This function exists to run multiple copies of optimize.portfolio, presumabley in parallel using foreach.
This is typically done to test your parameter settings, specifically total population size, but also possibly to help tune your convergence settings, number of generations, stopping criteria, etc.
If you want to use all the cores on your multi-core computer, use the parallel version of the apppropriate optimization engine, not this function.