Abstract:  Particle swarm optimization algorithm is introduced into a nonlinear financial risk model to solve the
problem that particle swarm optimization algorithm has low search ability and particle is easy to fall into local
optimization at the later stage of iteration． based on the optimization of inertia weight and the variation of
individual position of each particle，an improved particle swarm optimization algorithm is proposed． PSO
(Particle Swarm Optimization) is used to select the optimal control parameters to reduce the total risk value of
the financial system to the greatest extent． The simulation results show that the improved particle swarm
optimization algorithm is superior to the traditional particle swarm optimization algorithm in terms of global
optimization and search speed．

Key words:  , nonlinear, particle swarm, risk control, global optimum