Hierarchically constrained multi-fidelity blackbox optimization
arxiv(2023)
Abstract
This work introduces a novel multi-fidelity blackbox optimization algorithm
designed to alleviate the resource-intensive task of evaluating infeasible
points. This algorithm is an intermediary component bridging a direct search
solver and a blackbox, resulting in reduced computation time per evaluation,
all while preserving the efficiency and convergence properties of the chosen
solver. This is made possible by assessing feasibility through a broad range of
fidelities, leveraging information from cost-effective evaluations before
committing to a full computation. These feasibility estimations are generated
through a hierarchical evaluation of constraints, tailored to the
multi-fidelity nature of the blackbox problem, and defined by a biadjacency
matrix, for which we propose a construction. A series of computational tests
using the NOMAD solver on the Solar family of blackbox problems are conducted
to validate the approach. The results show a significant improvement in
solution quality when an initial feasible starting point is known in advance of
the optimization process. When this condition is not met, the outcomes are
contingent upon certain properties of the blackbox.
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