文献类型：期刊文献

英文题名：Nonlinear inertia weigh particle swarm optimization combines simulated annealing algorithm and application in function and SVM optimization

作者：Jiao, Bin[1]; Xu, Zhixiang[1,2]

机构：[1] Electric Engineering School, Shanghai DianJi University, Shanghai 200240, China; [2] College of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

年份：2012

卷号：130-134

起止页码：3467

外文期刊名：Applied Mechanics and Materials

收录：EI(收录号：20114714534995)

语种：英文

外文关键词：Parameter estimation - Iterative methods - Support vector machines - Particle swarm optimization (PSO)

摘要：This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing(SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM. ? (2012) Trans Tech Publications, Switzerland.