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A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Optimisation problems are ubiquitous in particle and astrophysics, and
involve locating the optimum of a complicated function of many parameters that
may be computationally expensive to evaluate. We describe a number of global
optimisation algorithms that are not yet widely used in particle astrophysics,
benchmark them against random sampling and existing techniques, and perform a
detailed comparison of their performance on a range of test functions. These
include four analytic test functions of varying dimensionality, and a realistic
example derived from a recent global fit of weak-scale supersymmetry. Although
the best algorithm to use depends on the function being investigated, we are
able to present general conclusions about the relative merits of random
sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance
Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf
Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and
Adaptive Memory Programming for Global Optimisation algorithms