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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Detalles Bibliográficos
Autores: Luis Gonzalo Guillén Anaya, Luis Alberto Rodríguez-Picón, Raúl Ñeco Caberta, ALEJANDRO ALVARADO-INIESTA
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:México
Institución:Universidad Autónoma de Ciudad Juárez
Repositorio:Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez
OAI Identifier:oai:uacj.mx:oai:cathi.uacj.mx:20.500.11961ir-4666
Acceso en línea:https://doi.org/10.1007/s10845-018-1432-9
Access Level:acceso abierto
Palabra clave:Structural optimization
Multi-objective optimization
Genetic programming
Finite element analysis
Decision making
Research Subject Categories::MATHEMATICS::Applied mathematics::Optimization, systems theory
Research Subject Categories::TECHNOLOGY::Industrial engineering and economy::Manufacturing engineering and work sciences::Manufacturing engineering
info:eu-repo/classification/cti/7
Descripción
Sumario: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.