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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Published in | Journal of intelligent manufacturing Vol. 31; no. 1; pp. 19 - 32 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
2020
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 0956-5515 1572-8145 |
DOI: | 10.1007/s10845-018-1432-9 |