Design Optimization of 3D Garment Style Ideas by Clustering Joint Fish Swarm Algorithm

Abstract

Clothing style is the core of apparel design, and three-dimensional clothing style design focuses on showing the effect of clothing appearance. The purpose of this paper is to organize consumer research data and use principal component analysis to extract consumer lifestyle factors. To obtain five types of consumer groups, the KFCM algorithm based on artificial fish school optimization uses the lifestyle factor as the clustering variable and performs rapid clustering. Analyze the differences in attitudes towards 3D clothing among the five types of consumers using ANOVA after extracting their attitude factor again. Analyze the steps of 3D clothing generation, combine 3D clothing parts using natural splicing algorithms, and consider the design factors that affect 3D clothing consumers to generate the 3D clothing styles that consumers like. The analysis of the influencing factors shows that the consumer group of “self-expressive fashionistas”-Type 1 has the most positive attitude towards 3D garments, with a mean value of 3.8695, while the consumer group of “rough-and-tumble comforts”-Type 3 has the lowest mean value, which indicates that the consumer group of Type 3 has the most positive attitude towards 3D garments, and the consumer group of Type 3 has the lowest mean value, which indicates that the consumer group of Type 3 has the lowest attitude towards 3D garments. 3D clothing receives the least positive feedback from the consumer group. The design of 3D clothing should actively incorporate fashion, health, popularity, and other design factors.