| Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 17, 2026
Multi-modal Information Learning and Analytics on Cross-Media Data Integration
|
|
|---|---|---|
| Article Number | 8 | |
| Number of page(s) | 18 | |
| DOI | https://doi.org/10.1051/smdo/2026007 | |
| Published online | 17 March 2026 | |
Research Article
Modeling and simulation optimization of interactive design systems based on artistic style transfer
1
School of Fashion and Art Design, Haojing College of Shaanxi University of Science and Technology, Xi’an 712000, Shaanxi, China
2
School of Economics and Trade, Haojing College of Shaanxi University of Science and Technology, Xi’an 712000, Shaanxi, China
* e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
29
August
2025
Accepted:
15
February
2026
Abstract
This paper tackles key challenges in interactive design systems: high latency, weak user control, and the aesthetic-function trade-off. We propose an optimization method integrating lightweight generative networks with dynamic modeling. First, a feedforward network architecture based on the MobileNetV3 encoder and the AdaIN (Adaptive Instance Normalization) decoder is designed to achieve millisecond-level style transfer. Second, based on probabilistic state-space modeling theory, a human-machine collaborative state machine is constructed. This Markov decision process describes the transition probabilities of user operation sequences and integrates hard constraints such as readability and layout rationality. Then, a user-system co-simulation framework is proposed. A virtual user behavior simulator generates diverse interaction sequences, driving the NSGA-III (Non-dominated Sorting Genetic Algorithm III) algorithm to perform multi-objective optimization on style quality, response latency, and constraint satisfaction. Experimental results demonstrate significant improvements over baseline methods (AdaIN, WCT2) in the system's consistency of style expression, real-time interaction, and design usability. After 50 generations of optimization, the average FID (Fréchet Inception Distance) value drops from 20.4 to 13.5; the interaction latency decreases from 286 ms to 187 ms; and the constraint violation rate drops from 22.3% to 5.7%, a decrease of 16.6 percentage points, validating the effectiveness of the “modeling-simulation-optimization” methodology. This method achieves a closed-loop collaboration between art generation and engineering design, providing a modeling, simulation, and optimization solution for intelligent interactive design systems.
Key words: Interactive design system / artistic style transfer / lightweight generative network / human–machine collaborative state machine / multi-objective simulation optimization
© L. Yan et al., Published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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