Nonlinear Inertia Weigh Particle Swarm Optimization combines Simulated Annealing Algorithm and Application in Function and SVM Optimization

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 PSOSVM.

关键词： Particle swarm optimization algorithm inertia weight simulated annealing algorithm function optimization parameter optimization

作者: JIAO Bin XU Zhixiang

作者单位: Electric Engineering School, Shanghai DianJi University, Shanghai 200240 Electric Engineering School, Shanghai DianJi University, Shanghai 200240 College of Information Scie

会议类型: 国际会议

会议名称: 2011 3nd International Conference on Mechanical and Electronics Engineering(2011年第三届机械与电子工程国际会议 ICMEE2011)

会议地点: 合肥

会议语种:英文

页码: 3467-3471

在线出版日期: 2011-09-23（万方平台首次上网日期，不代表论文的发表时间）