Surrogate model optimization

A julia function can be optimised with

smoptimize(f::Function, search_range::Array{Tuple{Float64,Float64},1}; options=Options())

Example

julia> using SurrogateModelOptim
julia> rosenbrock_2D(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
julia> search_range=[(-5.0,5.0),(-5.0,5.0)]
julia> smoptimize(rosenbrock_2D, search_range)

Due to the high cost of creating several surrogates it is highly advisable to create the surrogate model in parallel. Start julia in parallel with > julia -p x where x is the number of available cores. The previous example can then be run as

julia> result = smoptimize(rosenbrock_2D, search_range;
                    options=SurrogateModelOptim.Options(
                    iterations=25,
                    num_interpolants=N*x, #Where N is an integer number
                    num_start_samples=5,
                        ));

The default option num_interpolants=20 meaning the surrogate model ensemble contains 20 RBF interpolants has shown good performance for a variety of functions. More test functions and examples can be found in the source files under examples. The performance is typically good for smooth functions with or without noise. Discontinuous functions are not captured well by RBF interpolation, see the example folder to see this problem.