Machine Learning Model Based on Improved DBO Algorithm Optimized SVM

An innovative model on SVM is proposed, utilizing an enhanced version of the Dung beetle optimizer (DBO) algorithm in conjunction with Support Vector Machine (SVM) optimization. The enhanced DBO algorithm incorporates a Logistic chaotic mapping technique to improve population initialization, enhancing diversity and randomness. To further enhance its performance, strategies like Cauchy mutation and adaptive dynamic weighting are adopted to prevent premature convergence and local optima. This improved algorithm is then applied to SVM parameter optimization, resulting in improved classification performance. To validate the effectiveness of the proposed method, a model called HDBO-SVM is constructed and tested on the UCI dataset. The experimental results substantiate the outstanding performance of the HDBO-SVM model, showcasing a significant improvement compared to conventional models.