Modular design and algorithmic complexity optimization for large-scale software systems

The size and complexity of software systems in the field of intelligent software engineering are growing, and how to effectively organize and manage code has become a major challenge for software developers. This paper discusses the application of modular design in large-scale software systems, and studies the complexity and optimization strategies of modular design algorithms. User preferences are understood through the analysis and transformation of user requirements, which points out the direction for modular design of large-scale software systems. The particle swarm optimization algorithm based on probabilistic selection is used to modularize the design of large-scale software systems, and the complexity of the PSPSO algorithm is measured in terms of time complexity and space complexity. The modular design algorithm in this paper has a high degree of cohesion, a small degree of inter-module coupling, and a desired modularity greater than the comparison algorithm. At the same time, the complexity of the design algorithm shows a growing trend with the expansion of the size of the required problem, but it will decrease with the increase of the number of particle swarms, and the complexity of the algorithm can be optimized by methods such as dynamic programming and greedy algorithm.