A PSO and pattern search based memetic algorithm for SVMs parameters optimization

NEUROCOMPUTING

卷:117

Article

Bao, Yukun¹  Hu, Zhongyi¹  Xiong, Tao¹ 

[1] Huazhong Univ Sci & Technol, Sch Management, Dept Management Sci & Informat Syst, Wuhan 430074, Peoples R China

关键词: Parameters optimization;    Support vector machines;    Memetic algorithms;    Particle swarm optimization;    Pattern search;   

DOI  :  10.1016/j.neucom.2013.01.027

来源: Elsevier

【 摘 要 】

Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on particle swarm optimization algorithm (PSO) and pattern search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with optimum solutions, while pattern search (PS) is used to produce an effective exploitation on the potential regions obtained by PSO. Moreover, a novel probabilistic selection strategy is proposed to select the appropriate individuals among the current population to undergo local refinement, keeping a well balance between exploration and exploitation. Experimental results confirm that the local refinement with PS and our proposed selection strategy are effective, and finally demonstrate the effectiveness and robustness of the proposed PSO-PS based MA for SVMs parameters optimization. (C) 2013 Elsevier B.V. All rights reserved.

【 授权许可】

Free