Cauchy mutation for decision-making variable of Gaussian particle swarm optimization applied to parameters selection of SVM |
| |
| Authors: | Qi Wu |
| |
| Affiliation: | 1. Faculty of Electrical Engineering, J.J. Strossmayer University of Osijek, Kneza Trpimira 2b, 31000 Osijek, Croatia;2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia;1. Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China;2. Key Laboratory of Computational Intelligence and Chinese Information Processing, Shanxi University, Taiyuan 030006, China;1. College of Education Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China;2. College of Business and Administration, Zhejiang University of Technology, Hangzhou, 310023, China;3. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, 410082, China |
| |
| Abstract: | Due to the slow convergence of Gaussian particle swarm algorithm (GPSO) during parameters selection of support vector machine (SVM), this paper proposes a novel PSO with hybrid mutation strategy. Since random number generated from Cauchy distribution has better convergence characteristic than ones from Gaussian distribution during mutation strategy. Cauchy mutation is applied to amend the decision-making variable of Gaussian PSO. The adaptive mutation based on the fitness function value and the iterative variable is also applied to inertia weight of PSO. The results of application in parameter selection of support vector machine show the proposed GPSO with Cauchy mutation strategy 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 Gaussian PSO. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
