Traditionally

real-time optimization needs to reach steady state before being implemented. While

regarding multivariable and high dimensional systems global optimization is always time-consuming and too long to adjust the control parameters in real time.To solve these problems

this paper proposes a real-time evolution approach towards multilevel process system

which divides a multilevel process system into several interconnected subsystems.When the system is disturbed

the process waiting for steady state is divided into several pseudo-steady states.In each steady state

a particle swarm optimization algorithm is adapted to optimize each sub-system in turn

while the rest of sub-systems are regarded as the static system.Then coordinate the shared and internal variables between the sub-systems

and put the finally optimal solution into the controller to change the system set-point continuously.This paper gives the detailed algorithm steps and the two stage series reactor is carried out to demonstrate the benefits of the proposed methodology.