article

An IGA-based design support system for realistic and practical fashion designs

Authors:

John H. XinAuthors Info & Claims

Computer-Aided Design, Volume 45, Issue 11

Pages 1442 - 1458

https://doi.org/10.1016/j.cad.2013.06.014

Published: 01 November 2013 Publication History

Abstract

In this paper, a customised fashion design system is proposed for non-professional users (general customers) to create their preferred fashion designs in a user-friendly way. The proposed sketch design system consists of a sketch representation and composing method, an interactive genetic algorithm (IGA)-based design model, and a user-friendly interface. The sketch representation and composing method generates feasible design sketches, based on the design parameters defined by the IGA-based design model, and the sketches are presented to customers via the user-friendly interface. Experimental results have demonstrated that the proposed system is effective in generating fashion design sketches reflecting users' preference.

References

[1]

Lampel, J. and Mintzberg, H., Customizing customisation. Sloan Management Review. v38 i1. 21-30.

[2]

Senanayake MM. Mixed mass production and mass customization: best practices for apparel {Ph.D thesis}. North Caroline State University; 2004.

[3]

Franke, N. and Piller, F., Toolkits for user innovation and design: an exploration of user interaction and value creation. Journal of Product Innovation Management. v21. 401-415.

[4]

Tseng, M.M. and Jianxin, Jiao., Mass customization. In: Gavriel, Salvendy (Ed.), Handbook of industrial engineering: technology and operations management, John Wiley & Sons, New York. pp. 684-709.

[5]

Fiore, A.M., Lee, S.E and Kunz, G., Individual differences, motivations, and willingness to use mass customization options of fashion products. European Journal of Marketing. v28 i7. 835-849.

[6]

Piller, F.T., Moeslein, K. and Stotko, C.M., Does mass customization pay? An economic approach to evaluate customer integration. Production Planning & Control. v15. 435-444.

[7]

Piller, F., Observations on the present and future of mass customization. 2008. Springer Science+Business Media, LLC.

[8]

Kim, S. and Park, C.K., Parametric body model generation for garment drape simulation. Fibers and Polymers. v5 i1. 12-18.

[9]

Wang, C.C.L., Parameterization and parametric design of mannequins. Computer-Aided Design. v37 i1. 83-98.

[10]

Cho, Y., Komatsu, T., Inui, S., Takatera, M., Shimizu, Y. and Park, H., Individual pattern making using computerized draping method for clothing. Textile Research Journal. v76 i8. 646-654.

[11]

Wu, Yingying, Development of an intelligent patternmaking system for skirt design {Master of philosophy dissertation}. 2012. The Hong Kong Polytechnic University, Hong Kong.

[12]

Computer aided clothing pattern design with 3D editing and pattern alteration. Computer-Aided Design. v44. 721-734.

[13]

Kicinger, K., Arciszewski, T. and Jong, K.D., Evolutionary computation and structural design: a survey of the state-of-the-art. Computers and Structures. v83 i23-24. 1943-1978.

[14]

Lewis, M., Evolutionary visual art and design. In: Romero, J., Machado, P. (Eds.), The art of artificial evolution: a handbook on evolutionary art and music, Springer. pp. 3-37.

[15]

Wong, SSY and Chan, KCC, EvoArch: an evolutionary algorithm for architectural layout design. Computer-Aided Design. v41 i9. 649-667.

[16]

Takagi, H., Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE. v89 i9. 1275-1296.

[17]

Dawkins, R., The blind watchmaker. 1986. Longman Scientific and Technical, Harlow.

[18]

Bush, B.J. and Sayama, H., Hyperinteractive evolutionary computation. IEEE Transaction on Evolutionary Computation. v15 i3. 424-433.

[19]

Sato, T. and Hagiwara, M., IDSET: interactive design system using evolutionary techniques. Computer-Aided Design. v33 i5. 367-377.

[20]

Gu, Z.Y., Tang, M.X. and Frazer, J.H., Capturing aesthetic intention during interactive evolution. Computer-Aided Design. v38. 224-237.

[21]

Takagi, H. and Ohsaki, M., Interactive evolutionary computation-based hearing aid fitting. IEEE Transactions on Evolutionary Computation. v11 i3. 414-427.

[22]

Brintrup, A.M., Ramsden, J., Takagi, H. and Tiwari, A., Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms. IEEE Transactions on Evolutionary Computation. v12 i3. 343-354.

[23]

Sugahara, M., Miki, M. and Hiroyasu, T., Design of Japanese kimono using interactive genetic algorithm. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. 174-178.

[24]

Avigad, G. and Moshaiov, A., Interactive evolutionary multiobjective search and optimization of set-based concepts. IEEE Transactions on Systems, Man and Cybernetics, B. v39 i4. 1013-1027.

[25]

Hastings, E.J., Guha, R.K. and Stanley, K.O., Interactive evolution of particle systems for computer graphics and animation. IEEE Transactions on Evolutionary Computation. v13 i2. 418-432.

[26]

Zamani, F., Amani-Tehran, M. and Latifi, M., Interactive genetic algorithm-aided generation of carpet pattern. The Journal of the Textile Institute. v100 i6. 556-564.

[27]

Sun XY, Chen J, Ma XP, Gong DW. Grid-based knowledge-guided interactive genetic algorithm and its application to curtain design, In: Proceedings of the second world congress on nature and biologically inspired computing, 2010.

[28]

Gong, D.W., Hao, G.S., Zhou, Y. and Sun, X.Y., Interactive genetic algorithms with multi-population adaptive hierarchy and their application in fashion design. Applied Mathematics and Computation. v185. 1098-1108.

[29]

Adaptive interactive genetic algorithms with individual interval fitness. Progress in Natural Science. v18. 359-365.

[30]

Inui, S., A combined system of computer aided design and genetic algorithm for apparel designing. Sen-I Gakkaishi. v52. 605-611.

[31]

Hu, Z.H., Ding, Y.S., Zhang, W.B. and Yan, Q., An interactive co-evolutionary CAD system for garment pattern design. Computer-Aided Design. v40. 1094-1104.

[32]

Nakanishi, Y., Capturing preference into a function using interactions with a manual evolutionary design aid system. In: Late-breaking papers, pp. 133-138.

[33]

Ogata, Y. and Onisawa, T., Interactive clothes design support system. In: Proceedings of the 14th international conference on neural information processing, pp. 657-665.

[34]

Kim, H.S. and Cho, S.B., Application of interactive genetic algorithm to fashion design. Engineering Applications of Artificial Intelligence. v13 i6. 635-644.

[35]

Lee, J. and Steen, C., Technical sourcesbook for designers. 2010. Fairchild Books, New York.

[36]

WGSN www.wgsn.com; Stylesight www.stylesight.com; Peclers Paris www.peclersparis.com.

[37]

Huffman, C. and Kahn, BE., Variety for sale: mass customization or mass confusion?. Journal of Retailing. v74. 491-513.

[38]

Kamali, N. and Loker, S., Mass customization: online consumer involvement in product design. Journal of Computer-Mediated Communication. v7 i4.

[39]

Open design. http://www.opendesign.com/the_oda_platform/teigha_dot_net.

[40]

Hwang, S.J., . In: Standardization and integration of body scan data for use in the apparel industry {Ph.D disssertation}, North Carolina State University, USA.

[41]

Chen, J.S. and Hou, J.L., A combination genetic algorithm with applications on portfolio optimization. In: Advances in applied artificial intelligence, Springer-Verlag, Berlin Heidelberg. pp. 197-206.

[42]

Meghna, B.S. and Barbara, M., A case-based micro interactive genetic algorithm (CBMIGA) for interactive learning and search: methodology and application to groundwater monitoring design. Environmental Modeling & Software. v25 i10. l176-1187.

[43]

Bentley, P., An introduction to evolutionary design by computers. In: Evolutionary design by computer, pp. 1-73.

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cover image Computer-Aided Design

Computer-Aided Design Volume 45, Issue 11

November, 2013

246 pages

ISSN:0010-4485

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2013.

Publisher

Butterworth-Heinemann

United States

Publication History

Published: 01 November 2013

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Cited By

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https://dl.acm.org/doi/10.1145/3583131.3590351
Kielarova SPradujphongphet P(2023)Genetic Algorithm for Product Design Optimization: An Industrial Case Study of Halo Setting for Jewelry DesignAdvances in Swarm Intelligence10.1007/978-3-031-36622-2_18(219-228)Online publication date: 14-Jul-2023
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Zhao Y(2022)Design of Garment Style Recommendation System Based on Interactive Genetic AlgorithmComputational Intelligence and Neuroscience10.1155/2022/91321652022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9132165
Kielarova SPradujphongphet P(2020)Collaborative Product Design for Product Customization: An Industrial Case of Fashion ProductCooperative Design, Visualization, and Engineering10.1007/978-3-030-60816-3_5(37-46)Online publication date: 25-Oct-2020
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Dou RZhang YNan G(2019)Application of combined Kano model and interactive genetic algorithm for product customizationJournal of Intelligent Manufacturing10.1007/s10845-016-1280-430:7(2587-2602)Online publication date: 1-Oct-2019
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Qi XZhang JLiang C(2018)Multiple Attributes Group Decision-Making Approaches Based on Interval-Valued Dual Hesitant Fuzzy Unbalanced Linguistic Set and Their ApplicationsComplexity10.1155/2018/31727162018Online publication date: 22-Apr-2018
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Dou RZong CNan G(2016)Multi-stage interactive genetic algorithm for collaborative product customizationKnowledge-Based Systems10.1016/j.knosys.2015.10.01392:C(43-54)Online publication date: 15-Jan-2016
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Dou RZhang YNan G(2016)Customer-oriented product collaborative customization based on design iteration for tablet personal computer configurationComputers and Industrial Engineering10.1016/j.cie.2015.11.00799:C(474-486)Online publication date: 1-Sep-2016
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