Evolutionary Simulated Annealing Algorithm with Agent Modeling for Trivial Classification of Thyroid Datasets

This research presents a new approach to the trivial classification of thyroid datasets by combining the Evolutionary Simulated Annealing (ESA) algorithm with agent-based modeling. The proposed method leverages the optimization capabilities of ESA along with the dynamic and adaptable nature of agent-based modeling to enhance the accuracy and efficiency of thyroid dataset classification. By simulating the annealing process and agent interactions, the algorithm achieves improved convergence toward optimal solutions for thyroid dataset classification tasks. The experimental results demonstrate the effectiveness of the proposed approach in achieving high classification accuracy and robustness compared to traditional methods. This research contributes to the advancement of classification techniques for medical datasets, specifically addressing thyroid data analysis.