New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems
In this study, a new methodology, hybrid Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) with Deep Neural Network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objectiv...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2020-12-01
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| Series: | Journal of Low Frequency Noise, Vibration and Active Control |
| Online Access: | https://doi.org/10.1177/1461348419868322 |
| Summary: | In this study, a new methodology, hybrid Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) with Deep Neural Network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/R is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/R. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/R is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/R is simulated, respectively. It proved the potential of the HDNN&SPEA/R for stiffness parameter for powertrain mount systems optimization problem. |
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| ISSN: | 1461-3484 2048-4046 |