Abstract:

Support vector machine (SVM)parameter optimization selection is an important research direction, but there is still no systematic theory to guide the selection of the SVM parameters.Since optimizing the SVM parameters by the artificial fishswarm algorithm tends to fall into the small neighborhood of the approximate optimal solution,we design the AFMC algorithm  for the SVM parameter optimization.At the early stage,we use the better parallel optimization performance of the fishswarm algorithm to quickly gain the approximate optimal solution.Then we use the MonteCarlo algorithm for local searching to achieve a quick and effective strongapproximate optimal solution.The numerical experiments show that the proposed algorithm has better classification performance and faster searching speed,and it is effective and feasible in the SVM parameter optimization.

Key words: support vector machine (SVM);parameter optimization;artificial fish algorithm;MonteCarlo algorithm;approximate optimal solution