Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method. Issue 3 (4th March 2018)

• Record Type: Journal Article
• Authors: Darvishvand, Leila; Kamkari, Babak; Kowsary, Farshad
• Is Part Of: Engineering optimization. Volume 50:Issue 3(2018)
• Page Start: 452
• Page End: 470
• Publication Date: 2018-03-04
• Subjects: Radiant furnaces -- inverse radiation problem -- artificial neural networks -- genetic algorithm -- Monte Carlo method; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2017.1323889
• Languages: English
• ISSNs: 0305-215X

Abstract

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.