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

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...

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Main Author: ALVARADO-INIESTA, ALEJANDRO
Other Authors: Guillén Anaya, Luis Gonzalo
Format: Artículo
Language: English
Published: 2018
Subjects:
Online Access: http://cathi.uacj.mx/20.500.11961/4666
https://doi.org/10.1007/s10845-018-1432-9
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Summary: 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.