关键词: GM(1, λ)模型, 粒子群优化, 动态自适应惯性权重, 随机变异算子

Abstract: This paper proposes a new adaptive particle optimization algorithm with dynamic adaptive inertia weight and random mutation.During the running time，the inertia weight is determined by the particle’s fitness that makes the algorithm become dynamical and adaptive.By judging the local convergence，when the algorithm gets into the local convergence，it can start random mutation.The improved algorithm is applied to solve the gray model GM(1,1,λ)，experiments on concrete examples show that the modified algorithm has strongly improved the calculation accuracy of gray model GM(1,1,λ).

Key words: gray model GM(1, λ), particle swarm optimization, dynamic adaptive inertia weight, random mutation operator