Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10322832 | Expert Systems with Applications | 2011 | 7 Pages |
Abstract
On the basis of the slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a hybrid mutation strategy that integrates Gaussian mutation operator and Cauchy mutation operator for PSO. The combinatorial mutation based on the fitness function value and the iterative variable is also applied to inertia weight. The results of application in parameter selection of support vector machine show the proposed PSO with hybrid mutation strategy based on Gaussian mutation and Cauchy mutation is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than sole Gaussian mutation and standard PSO.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qi Wu, Rob Law,