Averaging principle for stochastic real Ginzburg-Landau equation driven by $ \alpha $-stable process

In this paper, we study a system of stochastic partial differential equations with slow and fast time-scales, where the slow component is a stochastic real Ginzburg-Landau equation and the fast component is a stochastic reaction-diffusion equation, the system is driven by cylindrical \begin{document}$ \alpha $\end{document} -stable process with \begin{document}$ \alpha\in (1, 2) $\end{document} . Using the classical Khasminskii approach based on time discretization and the techniques of stopping times, we show that the slow component strong converges to the solution of the corresponding averaged equation under some suitable conditions.