Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method

Leila Darvishvand, Babak Kamkari, Farshad Kowsary

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.

Original languageEnglish
Pages (from-to)452-470
Number of pages19
JournalEngineering Optimization
Volume50
Issue number3
DOIs
Publication statusPublished - 4 Mar 2018

Bibliographical note

Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • artificial neural networks
  • genetic algorithm
  • inverse radiation problem
  • Monte Carlo method
  • Radiant furnaces

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