COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite
This work provides a standardized benchmark for evaluating large-scale optimization algorithms, addressing a bottleneck in the field, though it is incremental as it builds on the existing bbob framework.
The paper tackles the challenge of scaling black-box optimization benchmarks to high dimensions by introducing the bbob-largescale test suite, which reduces computational and memory costs from quadratic to linear scaling while maintaining desired properties like non-separability and ill-conditioning.
The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, extends the well-known single-objective noiseless bbob test suite, which has been used since 2009 in the BBOB workshop series, to large dimension. The core idea is to make the rotational transformations R, Q in search space that appear in the bbob test suite computationally cheaper while retaining some desired properties. This documentation presents an approach that replaces a full rotational transformation with a combination of a block-diagonal matrix and two permutation matrices in order to construct test functions whose computational and memory costs scale linearly in the dimension of the problem.