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Comparison of Several Stochastic and Deterministic Derivative-Free Global Optimization Algorithms

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Mathematical Optimization Theory and Operations Research (MOTOR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11548))

Abstract

In this paper popular open-source solvers are compared against Globalizer solver, which is developed at the Lobachevsky State University. The Globalizer is designed to solve problems with black-box objective function satisfying the Lipschitz condition and shows competitive performance with other similar solvers. The comparison is done on several sets of challenging multi-extremal benchmark functions. Also this work considers a method of heuristic hyperparameters control for the Globalizer allowing to reduce amount of initial tuning before optimization. The proposed scheme allows substantially increase convergence speed of the Globalizer by switching between “local” and “global” search phases in runtime.

The study was supported by the Russian Science Foundation, project No. 16-11-10150.

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Notes

  1. 1.

    Software implementations of these generators are available in source codes at the page https://github.com/sovrasov/global-optimization-test-problems.

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Authors and Affiliations

  1. Lobachevsky State University of Nizhni Novgorod, Nizhni Novgorod, Russia

    Vladislav Sovrasov

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  1. Vladislav Sovrasov
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Correspondence to Vladislav Sovrasov .

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Editors and Affiliations

  1. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  2. Sobolev Institute of Mathematics, Novosibirsk, Russia

    Yury Kochetov

  3. University of Florida, Gainesville, FL, USA

    Panos Pardalos

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Sovrasov, V. (2019). Comparison of Several Stochastic and Deterministic Derivative-Free Global Optimization Algorithms. In: Khachay, M., Kochetov, Y., Pardalos, P. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2019. Lecture Notes in Computer Science(), vol 11548. Springer, Cham. https://doi.org/10.1007/978-3-030-22629-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-22629-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22628-2

  • Online ISBN: 978-3-030-22629-9

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