Osnaps

using Osnaps

# Define an objective function (using Ackley function here as an example)
function ackley(x::AbstractVector{T}) where T<:Real
    arg1 = 0.0
    arg2 = 0.0
    dims = length(x)
    @inbounds for i in eachindex(x)
        arg1 += abs2(x[i])
        arg2 += cospi(2.0 * x[i])
    end
    arg1  = 0.2 * sqrt(arg1 / dims)
    arg2 /= dims
    return -20.0 * exp(-arg1) - exp(arg2) + ℯ + 20.0
end

nd  = 15
lb  = ntuple(i -> -32.0, nd) # lower bounds
ub  = ntuple(i -> +32.0, nd) # upper bounds
obj = optimizer(nd)          # create an object for the optimization
minimize!(obj, ackley, lb, ub, avgtimes=3)

println(obj.xsol) # print the resulted solution