关键词: 粒子群优化算法, 惯性权重, 参数估计, 多元线性回归

Abstract: As for the flaw of standard PSO getting into early local optimum more easily, an improved PSO(NonLinear Decreasing Random Inertia Weight PSO)is proposed based on modifying the inertia weight of standard PSO. It is a new strategy of inertia weight to add the considering of random factors based on NonLinear Decreasing Inertia Weight. Experiments on the benchmark functions show that the performance of the improved PSO outperforms standard PSO. In order to solve the complex problems of the parameter estimation calculation for the multiple linear regression models, a novel method to estimate parameters is presented based on the improved particle swarm optimization algorithm in the paper. Maximum likelihood estimation is adopted as the fitness function for the optimization problem. Thus the model of calculating parameters for the multiple linear regression models is set up. Through a numerical simulation computational experiment, the effectivity and practicality of this method is demonstrated.

Key words: particle swarm optimization;inertia weight;parameter estimation;multiple linear regression models