Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach

Alejandro Alvarado-Iniesta (), Luis Gonzalo Guillen-Anaya, Luis Alberto Rodríguez-Picón and Raul Ñeco-Caberta
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Alejandro Alvarado-Iniesta: Universidad Autónoma de Ciudad Juárez
Luis Gonzalo Guillen-Anaya: Universidad Autónoma de Ciudad Juárez
Luis Alberto Rodríguez-Picón: Universidad Autónoma de Ciudad Juárez
Raul Ñeco-Caberta: Universidad Autónoma de Ciudad Juárez

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 1, No 3, 19-32

Abstract: Abstract This work addresses the optimization of an engine mount design from a multi-objective scenario. Our methodology is divided into three phases: phase one focuses on data collection through computer simulations. The objectives considered during the analyses are: total mass, first natural frequency and maximum von Mises stress. In phase two, a surrogate model by means of genetic programming is generated for each one of the objectives. Moreover, a local search procedure is incorporated into the overall genetic programming algorithm for improving its performance. Finally, in phase three, instead of steering the search to finding the approximate Pareto front, a local exploration approach based on a change in the weight space is used to lead a search into user defined directions turning the decision making more intuitive.

Keywords: Structural optimization; Multi-objective optimization; Genetic programming; Finite element analysis; Decision making (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-018-1432-9

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