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- Title
New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems.
- Authors
Dao, Dinh-Nam; Guo, Li-Xin
- Abstract
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.
- Subjects
AUTOMOBILE power trains; FORECASTING; EVOLUTIONARY algorithms; MATHEMATICAL optimization; STIFFNESS (Mechanics); GENETIC algorithms
- Publication
Journal of Low Frequency Noise, Vibration & Active Control, 2020, Vol 39, Issue 4, p850
- ISSN
1461-3484
- Publication type
Article
- DOI
10.1177/1461348419868322