Abstract A novel adaptive mutation particle swarm optimization algorithm with dynamic nonlinear changed inertia weight (AMPSO) was presented to accelerate searching speed and avoid premature convergence, which are mainly adjusted by inertia weight and information mutation. Firstly, the average particle spacing (APS) is employed to describe the population diversity, the smaller the APS, the more concentrated population and the worse species diversity. Then, inertia weight of each particle is updated as dynamic nonlinear based on APS to achieve a self-adaptive adjustment of global search ability and local search capabilities. Finally, information mutation based on slowly varying function (SV) introduced into the updating formula of PSO algorithm to expand search space. The proposed algorithm was tested compared with conventional PSO algorithms by three benchmark functions. The experiments show that the AMPSO has a stronger global optimization capability and higher search efficiency.