Product form evolutionary design system construction based on neural network model and multi-objective optimization

Article type: Research Article

Authors: Wu, Yixiang^{; *}

Affiliations: Yiwu Industrial and Commercial College, Yiwu, Zhejiang, China

Correspondence: [*] Corresponding author. Yixiang Wu, Yiwu Industrial and Commercial College, Yiwu 322000, Zhejiang, China. E-mail: 3316162376@qq.com.

Abstract: The product form evolutionary design based on multi-objective optimization can satisfy the complex emotional needs of consumers for product form, but most relevant literatures mainly focus on single-objective optimization or convert multiple-objective optimization into the single objective by weighting method. In order to explore the optimal product form design, we propose a hybrid product form design method based on back propagation neural networks (BP-NN) and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms from the perspective of multi-objective optimization. First, the product form is deconstructed and encoded by morphological analysis method, and then the semantic difference method is used to enable consumers to evaluate product samples under a series of perceptual image vocabularies. Then, the nonlinear complex functional relation between the consumers’ perceptual image and the morphological elements is fitted with the BP-NN. Finally, the trained BP-NN is embedded into the NSGA-II multi-objective evolutionary algorithm to derive the Pareto optimal solution. Based on the hybrid BP-NN and NSGA-II algorithms, a multi-objective optimization based product form evolutionary design system is developed with the electric motorcycle as a case. The system is proved to be feasible and effective, providing theoretical reference and method guidance for the multi-image product form design.

Keywords: Morphological analysis method, kansei engineering, back propagation neural networks, multi-objective evolutionary algorithm

DOI: 10.3233/JIFS-201439

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7977-7991, 2020