A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2
Source abstract
Abstract
As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method’s high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method’s good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Color perception and design · Color Science and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechatronics Engineering, Nanchang University, Nanchang 330031, China
School of Art and Design, Nanchang University, Nanchang 330031, China
Funding
National Natural Science Foundation of China
References (49)
Show source-provided references
1. Expression modes used by consumers in conveying desire for product form: A case study of a car10.1016/j.ergon.2005.06.004 ↗
2. Liu, C.Y., and Tong, L.I. (2018). Developing Automatic Form and Design System Using Integrated Grey Relational Analysis and Affective Engineering. Appl. Sci., 8.10.3390/app8010091 ↗
3. Kansei engineering as a tool for the design of in-vehicle rubber keypads10.1016/j.apergo.2016.12.019 ↗
4. Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems10.1016/j.cie.2019.01.055 ↗
5. Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design10.1016/j.ergon.2004.10.007 ↗
6. Emotional design method of product presented in multi-dimensional variables based on Kansei Engineering10.1080/09544828.2014.944488 ↗
7. Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design10.1016/j.aei.2018.02.002 ↗
8. Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design10.1016/j.cie.2011.01.011 ↗
9. Evolutionary design of product multi-image styling
10. Product form design model based on multiobjective optimization and multicriteria decision-making10.1155/2017/5187521 ↗
11. A methodology of integrating affective design with defining engineering specifications for product design10.1080/00207543.2014.975372 ↗
12. A posterior preference articulation approach to Kansei engineering system for product form design10.1007/s00163-018-0297-4 ↗
13. An improved SPEA2 algorithm with adaptive selection of evolutionary operators scheme for multiobjective optimization problems10.1155/2016/8010346 ↗
14. Wang, Z., Liu, W.D., Yang, M.L., and Han, D.J. (2019). Research on product image form design based on elliptic Fourier. Comput. Integr. Manuf. Syst., 1–23. Available online: http://kns.cnki.net/kcms/detail/11.5946.TP.20190222.0953.008.html/.
15. Using Improved Entropy-Cloud Model to Select High-Speed Railway Express Freight Train Service Sites10.1155/2017/7824835 ↗
16. Performance Evaluation in University Library’s E-resources Based on Entropy Weighted TOPSIS Method
17. Evaluation of variations in the shape of grain types using principal components analysis of the elliptic Fourier descriptors10.1016/j.compag.2011.10.016 ↗
18. Morphometric analysis of Passiflora leaves: The relationship between landmarks of the vasculature and elliptical Fourier descriptors of the blade
19. Hybrid method for estimating wind loads on ships based on elliptic Fourier analysis and radial basis neural networks10.1016/j.oceaneng.2016.06.031 ↗
20. The utility of elliptical Fourier analysis for estimating ancestry and sex from lateral skull photographs10.1016/j.forsciint.2018.06.009 ↗
21. Stability Evaluation of Fault Diagnosis Model Based on Elliptic Fourier Descriptor10.1155/2018/1238231 ↗
22. The human mandible in lateral view: Elliptical Fourier descriptors of the outline and their morphological analysis10.1016/S0940-9602(02)80021-8 ↗
23. A Fourier analysis based algorithm to separate touching kernels in digital images10.1016/j.biosystemseng.2010.10.011 ↗
24. Applying principal components analysis (PCA) for separating wingless birch fruits—A palaeoecological case study from northern Norway
25. A study that applies aesthetic theory and genetic algorithms to product form optimization10.1016/j.aei.2015.06.004 ↗
26. Quan, H.F., Li, S.B., and Hu, J.J. (2018). Product Innovation Design Based on Deep Learning and Kansei Engineering. Appl. Sci., 8.10.3390/app8122397 ↗
27. A kansei evaluation approach based on the technique of computing with words10.1016/j.aei.2015.11.001 ↗
28. A Comparison between the Linear Neural Network Method and the Multiple Linear Regression Method in the Modeling of Continuous Data10.4304/jcp.6.10.2143-2148 ↗
29. An optimizing BP neural network algorithm based on genetic algorithm10.1007/s10462-011-9208-z ↗
30. Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle10.1007/s00170-014-5606-0 ↗
31. Zitzler, E., Laumanns, M., and Thiele, L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology (ETH). Technical Report.
32. Statistics corner: A guide to appropriate use of correlation coefficient in medical research
33. Hybrid entropy-TOPSIS approach for energy performance prioritization in a rectangular channel employing impinging air jets10.1016/j.energy.2017.06.021 ↗
34. A robust design approach for enhancing the feeling quality of a product: A car profile case study10.1016/j.ergon.2004.10.008 ↗
35. Aesthetical design of a car profile: A Kano model-based hybrid approach10.1007/s00170-012-4636-8 ↗
36. Cheutet, V. (2019, February 22). 2D semantic sketcher for car aesthetic design [C]. Rabat: Fifth International Conference of Integrated Design and Production, 2007: 1–13. Available online: https://www.researchgate.net/publication/228537990_2D_semantic_sketcher_for_car_aesthetic_design.
37. A methodology for brand feature establishment based on the decomposition and reconstruction of a feature curve10.1016/j.aei.2018.05.008 ↗
38. Form design of product image using grey relational analysis and neural network models10.1016/j.cor.2004.03.021 ↗
39. Consumer-oriented product form design based on fuzzy logic: A case study of mobile phones10.1016/j.ergon.2007.03.003 ↗
40. A GABP optimized algorithm for filler rate of non-heated wire
41. Improved SPEA2 based on local search
42. Deb, K., and Jain, S. (2002, January 18–22). Running performance metrics for evolutionary multi-objective optimization. Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’02), Singapore.
43. Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000, January 18–20). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Proceedings of the Parallel Problem Solving from Nature VI Conference, Paris, France.10.1007/3-540-45356-3_83 ↗
44. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach10.1109/4235.797969 ↗
45. Assessing solution quality of biobjective 0-1 knapsack problem using evolutionary and heuristic algorithms10.1016/j.asoc.2009.08.037 ↗
46. A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem10.1016/j.eswa.2008.10.031 ↗
47. Weapon-target assignment based on improved SPEA2 algorithm
48. Zhou, A.M., Jin, Y.C., Zhang, Q.F., Sendhoff, B., and Tsang, E. (2006, January 16–21). Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada.
49. Method of product form design based on perceptual image10.3901/JME.2004.04.164 ↗