Multi-objective optimization problem (MaOP) is widely encountered in scientific research and engineering applications. Due to the exponential increase in the number of non-dominated solutions caused by objective conflicts, traditional multi-objective evolutionary algorithms face challenges such as increased computational complexity and degraded solution quality when solving MaOPs. To address these issues, this study proposes an improved two-archive high-dimensional multi-objective evolutionary algorithm based on information sharing, Two-Arch/IS, for the efficient solution of MaOP. The proposed algorithm leverages the inherent advantages of the two-archive framework, including low computational complexity and independent optimization of convergence and diversity. Distinct from traditional algorithms, archive maintenance in Two-Arch/IS is achieved through a subpopulation reflection and update strategy based on space partitioning, which enhances population diversity. Furthermore, an archive truncation strategy based on angle selection and shift-based density estimation is adopted to eliminate redundant solutions from the archive, thus maintaining selection pressure during the evolutionary process. Finally, a boundary-solution-driven information compensation mechanism is introduced to facilitate information exchange between the convergence and diversity archives, enabling effective complementarity among individuals in the population. In this study, Two-Arch/IS is benchmarked against several representative algorithms on 69 widely used test instances and real-world problems with 2 to 20 objectives. Experimental results demonstrate that the proposed algorithm effectively addresses the conflict between convergence and diversity in high-dimensional many-objective optimization and exhibits superior performance across multiple evaluation metrics.