Volume 4 Number 11 (Nov. 2009)
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JCP 2009 Vol.4(11): 1151-1158 ISSN: 1796-203X
doi: 10.4304/jcp.4.11.1151-1158

A Hybrid System Based on Neural Network and Immune Co-Evolutionary Algorithm for Garment Pattern Design Optimization

Zhi-Hua Hu
Logistics Research Center, Shanghai Maritime University, Shanghai 200135, China
Abstract—The purpose of this study is to develop a system to utilize the successful experiences and help the beginners of garment pattern design (GPD) by optimization methods. A hybrid algorithm (NN-ICEA) based on Neural Network (NN) and immune co-evolutionary algorithm (ICEA) to predict the fit of the garments and search optimal sizes. ICEA takes NN as fitness function and procedures including clonal proliferation, hyper-mutation and co-evolution search the optimal size values. Then, a series of experiments with a dataset of 450 pieces of garments are conducted to demonstrate the prediction and optimization capabilities of NN-ICEA. In the comparative studies, NN-ICEA is compared with NN-GA to show the value of immune inspired operators. Four types of GPD methods are summarized and compared. Moreover, the hybrid system for general features of garment is discussed. The fit prediction based on NN can achieve the high accuracy with the error rate less than 0.2. The size optimization based on ICEA works well when number of the missing sizes is less than 1/3 of the total size number. The research is a feasible and effective attempt aiming at a valuable problem and provides key algorithms for fit prediction and size optimization. The algorithms can be incorporated into garment CAD system.

Index Terms—Garment pattern design, Hybrid system, Neural network, Immune co-evolutionary algorithm, Fit garment

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Cite: Zhi-Hua Hu, "A Hybrid System Based on Neural Network and Immune Co-Evolutionary Algorithm for Garment Pattern Design Optimization," Journal of Computers vol. 4, no. 11, pp. 1151-1158, 2009.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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