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...
| Autores: | , , , |
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| 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 |
| 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. |
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