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New hybrid NSGA-III&SPEA/R to multi-object optimization in a half-car dynamic model

Dao, D.-N. and Guo, L.-X. (2020) New hybrid NSGA-III&SPEA/R to multi-object optimization in a half-car dynamic model. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234 (6). pp. 1660-1671. ISSN 9544070

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Abstract

In this article, we conducted a new hybrid method between Non-dominated Sorting Genetic Algorithm II (NSGA-III) and SPEA/R (HNSGA-III&SPEA/R). This method is implemented to find the optimal values of the powertrain mount system stiffness parameters. This is the task of finding multi-objective optimization involving six simultaneous optimization goals: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R has proposed with the integration of Strength Pareto evolutionary algorithm-based reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R and NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem. © IMechE 2019.

Item Type: Article
Divisions: Faculties > Faculty of Control Engineering
Identification Number: 10.1177/0954407019890164
Uncontrolled Keywords: Genetic algorithms; Optimal systems; Powertrains; Rigidity; Stiffness; Engine mount; Many-objective optimizations; Mount system; Multi-object optimization; Non-dominated sorting genetic algorithm - ii; Optimal solutions; Simultaneous optimization; Strength pareto evolutionary algorithm; Multiobjective optimization
Additional Information: Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9030

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