Chaos-Enhanced Multi-Objective Snow Melt Optimization for Precision Blending in Traditional Chinese Medicine Production

Abstract Ensuring uniform drug component quality during batch mixing is critical in Traditional Chinese Medicine (TCM) production, impacting product safety, efficacy stability and batch consistency. Challenges include raw material variability, equipment/process parameter differences, and QC standard limitations, leading to active ingredient fluctuations between batches. This study optimizes the TCM batch mixing process by applying an advanced Multi-Objective Snow Ablation Optimization (MOSAO) algorithm. By intelligently addressing complex multi-objective constraints—cost, efficiency, and quality uniformity—this algorithm has the potential to significantly enhance the robustness of the batch mixing process, thereby improving the quality consistency of Traditional Chinese Medicine formulations, ensuring the stability of their clinical efficacy, and reducing the risk of potential adverse drug reactions. To validate the performance of the improved algorithm, comparative experiments were conducted against other notable optimization algorithms proposed in recent years. The results showed that the Inverted Generational Distance (IGD) value was significantly superior to other comparison algorithms, and the HyperVolume (HV) value was also superior. In pharmaceutical batch mixing experiments, the optimization coefficients demonstrated greater robustness, and the magnitude of the optimization metrics far exceeded those of the comparison algorithms. The experimental results indicate that MOSAO achieves excellent mixing effects in Traditional Chinese Medicine batch mixing, realising cost reduction and efficiency improvement, and enhancing pharmaceutical production efficiency.