Optimizing the compliance third-party supervision workflow of involved enterprises using artificial intelligence ant colony algorithm.

This article optimized the compliance third-party supervision workflow of the involved enterprises based on the artificial intelligence ant colony optimization (ACO) algorithm. The basic principles and application advantages of ACO were introduced, and a heuristic information matrix was defined using ACO to optimize the data collection and analysis stage of the compliance third-party supervision workflow. During the experimental phase, a feasibility analysis was conducted on the optimization of third-party supervision workflows for compliance by ACO involved enterprises through simulation experiments. The experiments were evaluated from four aspects: data quality, model performance, scheme effectiveness, and supervision effectiveness. Among the metrics data for the ACO-optimized test set were 0.03 and 0.025 for MSE (Mean Square Error) and Γ, 0.8, 0.78, 0.79, and 0.88 for Accuracy, Recall, F1 Score, and AUC-ROC (Area Under the Curve-Receiver Operating Characteristic), and 0.28, 0.4, 0.88, and 0.12 for CER (Cost-Effectiveness Ratio), NPV (Net Present Value), SCR (Supervision Coverage Rate), and CRC (Compliance Rate Change), respectively. The experimental results showed that, in terms of data quality, model performance, scheme effectiveness, and supervision effectiveness, the evaluation indicators of the compliance third-party supervision workflow of the involved enterprises optimized using ACO were superior to those without ACO optimization.