Design Optimization of Men’s Suits Based on Consumer Preference

To assist designers in efficiently generating clothing designs that align with consumer preferences and enhancing overall design effectiveness through optimized combinations of design elements, this study proposes a consumer preference-based clothing optimization model. Taking men’s suits as a case study, key design elements and corresponding features were systematically extracted, and orthogonal experiments were employed to determine representative experimental samples. Consumer preference data were collected through perception experiments. Aiming to maximize consumer preference for clothing design, an integrated algorithm combining the BP neural network with the genetic algorithm was applied to achieve design optimization. The experimental results show that the evaluation of the optimization scheme is significantly better than that of other design schemes. The research method can be applied to the generation and optimization of clothing design schemes and has practical significance for improving consumers’ satisfaction with clothing designs.