Stochastic Tunneling for Improving the Efficiency of Stochastic Efficient Global Optimization

  • Fábio Nascentes
  • Rafael Holdorf LopezEmail author
  • Rubens Sampaio
  • Eduardo Souza de Cursi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


This paper proposes the use of a normalization scheme for increasing the performance of the recently developed Adaptive Target Variance Stochastic Efficient Global Optimization (sEGO) method. Such a method is designed for the minimization of functions that depend on expensive to evaluate and high dimensional integrals. The results showed that the use of the normalization in the sEGO method yielded very promising results for the minimization of integrals. Indeed, it was able to obtain more precise results, while requiring only a fraction of the computational budget of the original version of the algorithm.


Stochastic efficient global optimization Stochastic tunneling Global optimization Robust design 



The authors acknowledge the financial support and thank the Brazilian research funding agencies CNPq and CAPES.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Center for Optimization and Reliability in Engineering (CORE)Universidade Federal de Santa CatarinaFlorianópolisBrazil
  2. 2.Departamento de Áreas AcadêmicasInstituto Federal de Educação, Ciência e Tecnologia de Goiás-IFGJataíBrazil
  3. 3.Departamento de Engenharia MecânicaPUC-RioRio de JaneiroBrazil
  4. 4.Department MecaniqueInstitut National des Sciences Appliquees (INSA) de RouenSaint Etienne du Rouvray CedexFrance

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