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
UPDATE: MVRSM now contains a multi-objective version to deal with more than one objective, see the branch 'multi-objective'
@inproceedings{10.1145/3449726.3463136,
author = {Bliek, Laurens and Guijt, Arthur and Verwer, Sicco and de Weerdt, Mathijs},
title = {Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints},
year = {2021},
isbn = {9781450383516},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3449726.3463136},
doi = {10.1145/3449726.3463136},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {1851–1859},
numpages = {9},
location = {Lille, France},
series = {GECCO '21}
}- 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.