Automatic coloring of Ulos motif using large language model and non-dominated sorting differential evolution algorithm

Abstract

Ulos is a traditional Batak textile with high cultural significance, yet artisans lack tools to modernize designs while preserving heritage. Previous research primarily uses single-objective methods that fail to accommodate user preferences. This study introduces the first multi-objective framework for traditional textile coloring, differentiating itself by using a Large Language Model (LLM) as a natural language interface for non-technical weavers. The LLM translates user design preferences into dynamic objective functions to guide a Non-dominated Sorting Differential Evolution (NSDE) algorithm. Performance is assessed using the epsilon (ϵ) indicator, aesthetic metrics (contrast and colorfulness), and user preference scores. The system achieved high convergence with an ϵ-indicator value of 2.1788×10^(-3) and user preference scores reaching 0.66. Additionally, a paired t-test (p=0.07) demonstrated the algorithm's robustness across parameter variations. This approach enables artisans to create culturally authentic, aesthetically optimized designs through intuitive interaction.