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KŮDELA, J. MATOUŠEK, R.
Original Title
Combining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems
Type
journal article in Web of Science
Language
English
Original Abstract
Standard evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward and computationally cheap. However, in many real-world optimization problems, these evaluations involve computationally expensive numerical simulations or physical experiments. Surrogate-assisted evolutionary algorithms (SAEAs) have recently gained increased attention for their performance in solving these types of problems. The main idea of SAEAs is the integration of an evolutionary algorithm with a selected surrogate model that approximates the computationally expensive function. In this paper, we propose a surrogate model based on a Lipschitz underestimation and use it to develop a differential evolution-based algorithm. The algorithm, called Lipschitz Surrogate-assisted Differential Evolution (LSADE), utilizes the Lipschitz-based surrogate model, along with a standard radial basis function surrogate model and a local search procedure. The experimental results on seven benchmark functions of dimensions 30, 50, 100, and 200 show that the proposed LSADE algorithm is competitive compared with the state-of-the-art algorithms under a limited computational budget, being especially effective for the very complicated benchmark functions in high dimensions.
Keywords
Lipschitz surrogate model; Differential evolution; Radial basis function; Surrogate assisted evolutionary algorithms; High-dimensional expensive optimization
Authors
KŮDELA, J.; MATOUŠEK, R.
Released
17. 11. 2022
ISBN
0020-0255
Periodical
INFORMATION SCIENCES
Year of study
619
Number
January
State
United States of America
Pages from
457
Pages to
477
Pages count
21
URL
https://www.sciencedirect.com/science/article/pii/S0020025522013342
BibTex
@article{BUT179504, author="Jakub {Kůdela} and Radomil {Matoušek}", title="Combining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems", journal="INFORMATION SCIENCES", year="2022", volume="619", number="January", pages="457--477", doi="10.1016/j.ins.2022.11.045", issn="0020-0255", url="https://www.sciencedirect.com/science/article/pii/S0020025522013342" }