Abstract: The traditional MPPT algorithm has the problem of easily falling into local optima,and the intelligent optimization algorithms currently used to solve this type of problem also have shortcomings such as insufficient tracking accuracy and slow tracking speed. To improve the above shortcomings,this article proposes a hybrid optimization algorithm based on the tuna swarm algorithm（TSO）and the multi-strategy improved slime mould algorithm（MSMA）. This method accelerates the search speed through the parabolic feeding strategy of the early tuna algorithm and improves the slime mold algorithm by using a reverse learning strategy based on chaotic mapping to expand the exploration range of the algorithm,making it less prone to falling into local optima,and improving the algorithm’s operational speed. The improved algorithm is applied to the photovoltaic system MPPT,and the simulation results show that compared to the individual TSO and MSMA algorithms,the improved algorithm has a significant improvement in tracking speed under different shading conditions,with higher accuracy than the individual TSO and MSMA algorithms,and has better tracking speed and accuracy.