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2019 | OriginalPaper | Chapter

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

Author : Vladislav Sovrasov

Published in: Mathematical Optimization Theory and Operations Research

Publisher: Springer International Publishing

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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.

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previous chapter A Dynamic Algorithm for Constructing the Dual Representation of a Polyhedral Cone

next chapter On Some Methods for Strongly Convex Optimization Problems with One Functional Constraint

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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Title: Comparison of Several Stochastic and Deterministic Derivative-Free Global Optimization Algorithms
Author: Vladislav Sovrasov
Publisher: Springer International Publishing
Book: Mathematical Optimization Theory and Operations Research
Print ISBN: 978-3-030-22628-2

Electronic ISBN: 978-3-030-22629-9

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-22629-9_6

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