benchmarkfcns.boxbetts

benchmarkfcns.boxbetts(arg0: Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]', 'flags.c_contiguous']) → Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']

Computes the value of the Box-Betts Quadratic Sum benchmark function. SCORES = boxbetts(X) computes the value of the Box-Betts function at point X. boxbetts accepts a matrix of size M-by-3 and returns a vector SCORES of size M-by-1. Properties:

• Global minimum: 0

• Location of global minimum: (1, 10, 1)

• Number of dimensions: 3

• Recommended domain: [0.9, 1.2] x [9, 11.2] x [0.9, 1.2]

• Modality: multimodal

Mathematical Definition

\[f(x_1, x_2, x_3) = \sum_{i=1}^{10} \left[ e^{-0.1(i+1)x_1} - e^{-0.1(i+1)x_2} - (e^{-0.1(i+1)} - e^{-(i+1)})x_3 \right]^2\]

Visualization

No visualization available for this function.