Improving exploration strategies in large dimensions and rate of convergence of global random search algorithms

Noonan, Jack and Zhigljavsky, Anatoly ORCID: https://orcid.org/0000-0003-0630-8279 2024. Improving exploration strategies in large dimensions and rate of convergence of global random search algorithms. Journal of Global Optimization 88 , pp. 1-26. 10.1007/s10898-023-01308-6

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Abstract

We consider global optimization problems, where the feasible region X is a compact subset of Rd with d≥10 . For these problems, we demonstrate that the actual convergence of global random search algorithms is much slower than that given by the classical estimates, based on the asymptotic properties of random points, and that the usually recommended space exploration schemes are inefficient in the non-asymptotic regime. Moreover, we show that uniform sampling on entire X is much less efficient than uniform sampling on a suitable subset of X , and that the effect of replacement of random points by low-discrepancy sequences can be felt in small dimensions only.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Publisher:	Springer
ISSN:	0925-5001
Date of First Compliant Deposit:	11 July 2023
Date of Acceptance:	17 June 2023
Last Modified:	05 Feb 2024 15:52
URI:	https://orca.cardiff.ac.uk/id/eprint/160948

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