simple_sbo implements a Surrogate-Based Optimization (SBO) algorithm with experience from the following work:
J.H. Bussemaker et al., "Effectiveness of Surrogate-Based Optimization Algorithms for System Architecture Optimization", AIAA Aviation 2021, DOI: 10.2514/6.2021-3095
The algorithm treats discrete variables as continuous variables during infill search, however is specifically adapted for architecture optimization by applying the repair operator during sampling and infill search.
Either an RBF or a Kriging surrogate model is used. Kriging has as advantage that it also can predict the amount of uncertainty next to the model mean, and therefore allows for more interesting infill criteria. RBF, however, is faster to train and evaluate.
pip install -e .[simple_sbo]
The algorithm is implemented as a pymoo algorithm and already includes all relevant architecture optimization measures. It can be used directly with pymoo's interface:
from pymoo.optimize import minimize
from sb_arch_opt.algo.simple_sbo import get_simple_sbo_krg, get_simple_sbo_rbf
problem = ... # Subclass of ArchOptProblemBase
# Get Kriging or RBF algorithm
n_init = 100
simple_krg_sbo_algo = get_simple_sbo_krg(init_size=n_init, use_mvpf=True, use_ei=False, min_pof=.5)
simple_rbf_sbo_algo = get_simple_sbo_rbf(init_size=n_init)
# Enable intermediate results storage
results_folder_path = 'path/to/results/folder'
simple_krg_sbo_algo.store_intermediate_results(results_folder_path)
# Start from previous results (skipped if no previous results are available)
if simple_krg_sbo_algo.initialize_from_previous_results(problem, results_folder_path):
# No need to evaluate any initial points, as they have been previously evaluated
n_init = 0
n_infill = 10
result = minimize(problem, simple_krg_sbo_algo, termination=('n_eval', n_init+n_infill))