Abstract
This article proposes a ‘dynamic’ artificial bee colony (D-ABC) algorithm for solving optimizing problems. It overcomes the poor performance of artificial bee colony (ABC) algorithm, when applied to multi-parameters optimization. A dynamic ‘activity’ factor is introduced to D-ABC algorithm to speed up convergence and improve the quality of solution. This D-ABC algorithm is employed for multi-parameters optimization of support vector machine (SVM)-based soft-margin classifier. Parameter optimization is significant to improve classification performance of SVM-based classifier. Classification accuracy is defined as the objection function, and the many parameters, including ‘kernel parameter’, ‘cost factor’, etc., form a solution vector to be optimized. Experiments demonstrate that D-ABC algorithm has better performance than traditional methods for this optimizing problem, and better parameters of SVM are obtained which lead to higher classification accuracy.
Highlights
Artificial bee colony (ABC) algorithm was first proposed by Karaboga in 2005 [1]
Dynamic artificial bee colony (D-ABC) algorithm is introduced to improve the disadvantages of traditional ABC algorithms: poor convergence rate and local optimizing
D-ABC algorithm is utilized for multi-parameters optimization of support vector machine (SVM) classifier
Summary
Artificial bee colony (ABC) algorithm was first proposed by Karaboga in 2005 [1]. It has many advantages than earlier swarm intelligence algorithms, especially for constrained optimization problem. Karaboga and Akay [2] introduced modification rate (MR) factor to randomly modify more elements of the solution vector in each cycle, robustness of the algorithm is not quite well. Based on structural risk minimization principle, support vector machine (SVM) was first proposed by Cortes and Vapnik [10] in the 1990s It has many advantages on classification, but multiple parameters have to be properly selected. For a specific set of training samples, once classification accuracy is employed as objective function ~x, solution vector ~x is formed by parameters of SVM, training of SVM classifier could be transformed into a multi-parameters optimization problem. The main contributions of this article are (1) a modified ABC algorithm is proposed, named D-ABC algorithm; (2) D-ABC algorithm is applied to multi-parameters optimization of SVM soft-margin classifier.
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