A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm

TL;DR

This paper proposes a parallel ant colony optimization method to efficiently select optimal parameters for Support Vector Machines, enhancing their performance in various classification tasks.

Contribution

It introduces a novel parallel ACO approach for SVM parameter tuning, combining ant colony and parallel algorithms for improved optimization efficiency.

Findings

Enhanced SVM parameter selection accuracy

Reduced optimization time through parallel processing

Improved classification performance in experiments

Abstract

A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters $\delta$ and c in the classification function. That is to say,to optimize the SVM algorithm, the optimization of the two parameters play a huge role. Ant Colony Optimization (ACO) is optimization algorithm which simulate ants to find the optimal path.In the available literature, we mix the ACO algorithm and Parallel algorithm together to find a well parameters.