Meihui Zheng
ABSTRACT
With the continuous growth of sci & tech, digitalization has become the general trend of social and economic growth, and 3D virtual simulation technology is increasingly used in the growth of clothing industry. Traditional genetic algorithm (GA) can only solve the problem of system optimization with explicit expression of performance index, because a fitness function with explicit expression is needed to evaluate the fitness of evolutionary individuals. In this article, interactive genetic algorithm (IGA) and virtual simulation technology are introduced into real fashion design, and the constraint mechanism of fashion design is introduced into genetic operation to ensure the rationality of design styles and meet the aesthetic needs of users for 3D clothing styles. The simulation results show that after many iterations, the accuracy of IGA is better than that of traditional GA, reaching more than 95%, and the error is also significantly reduced. This has laid a good foundation for the overall improvement of clothing design effectiveness.
- Seo K. Comparison of virtual bodies for the purpose of fashion design in virtual spaces - Using apparel 3D-CADs. Journal of the Japan Research Association for Textile End-Uses, vol. 59, no. 8, pp. 629-635, 2018.Google Scholar
- Peters L D. On Fat Clothes: Unravelling Plus-Size Design Discourse. Design issues, vol. 2022, no. 1, pp. 38, 2022.Google Scholar
- Renna P. A Decision Investment Model to Design Manufacturing Systems based on a genetic algorithm and Monte-Carlo simulation. International Journal of Computer Integrated Manufacturing, vol. 30, no. 6, pp. 590-605, 2017.Google ScholarDigital Library
- Kim B K, Kim G, Lee S Y. Style-Controlled Synthesis of Clothing Segments for Fashion Image Manipulation. IEEE Transactions on Multimedia, vol. 22, no. 2, pp. 298-310, 2019.Google ScholarDigital Library
- Chen J. Research on evaluating the design effect of clothing and accessories with 2-tuple linguistic information. Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2059-2066, 2019.Google ScholarCross Ref
- Yang T, Jin G, Zhu J. design of image-side telecentric free form imaging systems based on a point-by-point construction-iteration process. Chinese optics letters, vol. 15, no. 6, pp. 77-81, 2017.Google Scholar
- Li Y, He Z, Wang S, Multideep Feature Fusion Algorithm for Clothing Style Recognition. Wireless Communications and Mobile Computing, vol. 2021, no. 4, pp. 1-14, 2021.Google Scholar
- Wang W, Fang Y, Nagai Y, Integrating Interactive Clothing and Cyber-Physical Systems: A Humanistic Design Perspective. Sensors, vol. 20, no. 1, pp. 127, 2019.Google ScholarCross Ref
- Kim S, Lee S, Jeong W. EMG Measurement with Textile-Based Electrodes in Different Electrode Sizes and Clothing Pressures for Smart Fashion Design Optimization. Polymers, vol. 12, no. 10, pp. 2406, 2020.Google ScholarCross Ref
- Qin Z. Clothing Accessory Design Based on Sensory Experience. NeuroQuantology, vol. 16, no. 6, pp. 460-465, 2018.Google Scholar
- Grabowska G, Struszczyk M, Baszczyk J, Operational Qualification of the Individualisation of Protective Fashion Design Based on the 3D Scanning Technique. Fibres and Textiles in Eastern Europe, vol. 28, no. 3, pp. 89-97, 2020.Google Scholar
- Fu S. Clothing fabric reengineering design from the perspective of analytical design aesthetics. Basic & clinical pharmacology & toxicology. vol. 2019, no. 1, pp. 124, 2019.Google Scholar
- Bodek A. Design and Development of Denim Fabrics with Improved Strength and Impact Abrasion Resistance for Motorcyclist Clothing. Fibres and Textiles in Eastern Europe, vol. 26, no. 1, pp. 53-58, 2018.Google Scholar
Index Terms
- Research on 3D Fashion Design System Combining Interactive Genetic Algorithm and Virtual Simulation Technology
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