New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

Dinh-Nam Dao,Li-Xin Guo

doi:10.1177/1461348419868322

Dinh-Nam Dao, Li-Xin Guo

Open Access

https://doi.org/10.1177/1461348419868322

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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.

Highlights

Multi-objective evolutionary algorithms (MOEAs) are common tools for solving multi-objective optimization problems in the technical field, because of their performance on issues with large design spaces and scenes difficult exercise
This study reported using a specific machine translation to represent the test that SPEA2 was chosen as the optimization method
HDNN&Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) algorithm has very fast computation time compared to SPEA/R algorithm

Summary

Introduction

Multi-objective evolutionary algorithms (MOEAs) are common tools for solving multi-objective optimization problems in the technical field, because of their performance on issues with large design spaces and scenes difficult exercise. This is the hybrid between the genetic algorithm (GA), deep neural network (DNN), and strength Pareto evolutionary algorithm-based reference direction for multi-objective (SPEA/R) to find the best of the Pareto-optimal front. This combination significantly reduces the number of samples needed for the training of deep ANNs. The performance of the new algorithm is demonstrated via some complex benchmark functions and for powertrain mount system stiffness parameter optimization problem with six-objective optimization in model 3D.

Results

Conclusion