An Investigation of the Hybridization of DE and BFGS Algorithms
Abstract
Many real-world global optimization problems are presented by a black-box model, therefore there is no information on properties of the objective function and its derivatives. Differential evolution (DE) is one of search algorithms, which demonstrates the high performance in solving global black-box optimization problems. Usually DE shows good global convergence, but it is slow in the local convergence. A local search can efficiently find a local optimum with high accuracy but cannot identify a basin of the global optimum. In the study we have proposed a hybrid of DE and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In BFGS derivatives are substituted by their approximations using the finite difference method. We have tested 3 different schemes of the cooperation of DE and BFGS. Using a set of benchmark optimization problems, the experimental results have shown that the hybridization can improve the performance of the standard DE algorithm. At the same time the choice of the hybridization scheme affects the results.
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About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Sopov, A., & Sherstnev, P. (2023). An Investigation of the Hybridization of DE and BFGS Algorithms. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 336-342). European Publisher. https://doi.org/10.15405/epct.23021.41