MVRSM uses a piece-wise linear surrogate model for optimisation of expensive cost functions with continuous and discrete variables.
MVRSM_minimize(obj, x0, lb, ub, num_int, max_evals, rand_evals) solves the minimisation problem
min f(x)
st. lb<=x<=ub, the first num_int variables of x are integer
where obj is the objective function, x0 the initial guess,
lb and ub are the bounds, num_int is the number of integer variables,
and max_evals is the maximum number of objective evaluations (rand_evals of these
are random evaluations).
It is the mixed-variable version that is related to the DONE algorithm, meant for problems with both continuous and discrete variables.
Laurens Bliek, 06-03-2020
- numpy
- scipy
- matplotlib
- time
- For comparison with other methods: hyperopt, gpy, pandas
Run the file demo.py directly, or indicate an existing test function to optimise, for example:
python demo.py -f dim10Rosenbrock -n 10 -tl 4
Here, -f is the function to be optimised, -n is the number of iterations, and -tl is the total number of runs.
Currently the following test functions are already supported:
'func2C', 'func3C', 'dim10Rosenbrock', 'linearmivabo', 'dim53Rosenbrock', 'dim53Ackley', 'dim238Rosenbrock'
Afterward, use plot_result.py for visualisation (set the correct folder and other parameters inside the file).
Please contact l.bliek@tue.nl if you have any questions.