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A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

Published version
Peer-reviewed

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Authors

Balázs, C 
van Beekveld, M 
Caron, S 
Dillon, BM 
Farmer, B 

Abstract

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.

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Keywords

Phenomenology of Field Theories in Higher Dimensions, Supersymmetry Phenomenology

Journal Title

Journal of High Energy Physics

Conference Name

Journal ISSN

1029-8479
1029-8479

Volume Title

2021

Publisher

Springer Science and Business Media LLC