Stochastic efficient global optimization with high noise variance and mixed design variables

López, Rosalía Rodríguez; Bismut, Elizabeth; Straub, Daniel

doi:10.1007/s40430-022-03920-1

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…This study demonstrates a novel technique for generating large datasets for the development of data-driven machine-learning models. Lopez et al (2023) used a stochastic efficient global optimization method with add high noise variance, are proposed two additional stopping criteria for the Monte Carlo integration that is required to approximate the objective function.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of reinforced concrete columns with variable circular cross-section hollowed using the criterion of instability and mechanical strength

Mattias

Araujo

2023

MMMS

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PurposeThis study aims to optimize the structural design of reinforced concrete columns with variable hollow circular sections.Design/methodology/approachThe columns were optimized according to the criteria of instability (buckling) and mechanical strength (compression and/or tensile strength). To perform the optimizations, routines are developed in Python using the penalty and sequential linearization programming (SLP) function methods to optimize the elements satisfying the buckling and stress criteria.FindingsAt the end of the optimization process, the optimal section is obtained for the example of a circular column with a variable section, this section has an average radius of 5% smaller than that initially defined.Originality/valueThe theoretical basis for column optimization and the structuring of an algorithm in Python language for the computational resolution of these problems are presented in a didactic way, as well as the comparative efficiency of the methods.

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Section: Introductionmentioning

confidence: 99%

“…Lopez et al . (2023) used a stochastic efficient global optimization method with add high noise variance, are proposed two additional stopping criteria for the Monte Carlo integration that is required to approximate the objective function.…”

Section: Introductionmentioning

confidence: 99%