Journal
JOURNAL OF HIGH ENERGY PHYSICS
Volume -, Issue 5, Pages -Publisher
SPRINGER
DOI: 10.1007/JHEP05(2021)108
Keywords
Phenomenology of Field Theories in Higher Dimensions; Supersymmetry Phenomenology
Categories
Funding
- Science and Technology Facilities Council [ST/T000864/1]
- Elusives ITN (Marie Sklodowska-Curie grant) [674896]
- SOM Sabor y origen de la Materia [FPA 2017-85985-P]
- Netherlands eScience Center
- NSFC Research Fund for International Young Scientists [11950410509]
- Australian Research Council (ARC) [DP180102209]
- ARC Centre of Excellence for Dark Matter Particle Physics [CE200100008]
- ARC Future Fellowship [FT190100814]
- Spanish MICIU/AEI [PGC2018-094856-B-I00]
- European Union/FEDER
- University of Valencia [APOSTD/2019/165]
- Generalitat Valenciana
- European Union
- Spanish Plan Nacional I+D+i [TIN2016-76406-P, PID2019-106827GB-I00/AEI/10.13039/501100011033]
- STFC [ST/T000864/1] Funding Source: UKRI
- Australian Research Council [FT190100814] Funding Source: Australian Research Council
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The research focused on optimization problems prevalent in particle and astrophysics, evaluating various global optimization algorithms and drawing general conclusions on their relative merits for different functions.
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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