ISSN : 1796-203X
Volume : 4    Issue : 11    Date : November 2009

A Hybrid System Based on Neural Network and Immune Co-Evolutionary Algorithm for Garment
Pattern Design Optimization
Zhi-Hua Hu
Page(s): 1151-1158
Full Text:
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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