Efficient global optimization of constrained mixed variable problems

Julien Pelamatti (), Loïc Brevault, Mathieu Balesdent, El-Ghazali Talbi and Yannick Guerin
Additional contact information
Julien Pelamatti: ONERA
Loïc Brevault: ONERA
Mathieu Balesdent: ONERA
El-Ghazali Talbi: Inria Lille - Nord Europe
Yannick Guerin: Centre national d’études spatiales Direction des lanceurs

Journal of Global Optimization, 2019, vol. 73, issue 3, No 7, 583-613

Abstract: Abstract Due to the increasing demand for high performance and cost reduction within the framework of complex system design, numerical optimization of computationally costly problems is an increasingly popular topic in most engineering fields. In this paper, several variants of the Efficient Global Optimization algorithm for costly constrained problems depending simultaneously on continuous decision variables as well as on quantitative and/or qualitative discrete design parameters are proposed. The adaptation that is considered is based on a redefinition of the Gaussian Process kernel as a product between the standard continuous kernel and a second kernel representing the covariance between the discrete variable values. Several parameterizations of this discrete kernel, with their respective strengths and weaknesses, are discussed in this paper. The novel algorithms are tested on a number of analytical test-cases and an aerospace related design problem, and it is shown that they require fewer function evaluations in order to converge towards the neighborhoods of the problem optima when compared to more commonly used optimization algorithms.

Keywords: Mixed continuous/discrete optimization; Efficient global optimization; Constrained optimization; Expensive black-box functions (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10898-018-0715-1

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