Abstract: The parrot optimization(PO)algorithm is a metaheuristic algorithm derived from the four behaviors of the Pyrrhura Molinae parrots. The PO algorithm tends to fall into local optima and suffers from insufficient convergence accuracy. To address these issues,this paper introduces an improved multi-strategy enhanced parrot optimizer(GSSPO),which integrates logistic chaotic mapping,adaptive spiral search and golden sine strategies. This algorithm can effectively enhance the global search capability and improve the convergence speed. Simulation experiments were conducted on five classic benchmark functions using the GSSPO algorithm and five other algorithms. The results confirmed the effectiveness and competitiveness of GSSPO. Furthermore,when applied to the optimization of a corrugated bulkhead design problem,the GSSPO algorithm demonstrated the ability to achieve optimal solutions with a moderate convergence rate.