Paper Info
Title
Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design.
Abstract
Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance; in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.
Year
DOI
Venue
2018
10.1016/j.aei.2018.02.002
Advanced Engineering Informatics
Keywords
Field
DocType
Product form design,Kansei engineering,Multi-objective evolutionary algorithms,Support vector regression
Data mining,Mathematical optimization,Evolutionary algorithm,Expert system,Kansei engineering,Multi-objective optimization,Optimal design,Engineering,Product design,Cluster analysis,Pareto principle
Journal
Volume
ISSN
Citations 
36
1474-0346
4
PageRank 
References 
Authors
0.45
12
3
Name
Order
Citations
PageRank
Meng-Dar Shieh11079.82
Yongfeng Li241.13
Chih-Chieh Yang312713.88