The artificial immune algorithm is an intelligent optimization algorithm derived from the working mechanism of biological immune systems. It simulates immune response processes including biological antigen recognition, antibody generation, clonal selection, immune memory, and immune suppression, and searches for optimal solutions through iterative population evolution. In recent years, artificial immune algorithms have been gradually applied in various fields such as industrial optimization, artificial intelligence, smart cities, and automatic control, showing favorable application value in scenarios including multi-objective constrained optimization, dynamic system scheduling, fault feature identification, and adaptive parameter tuning. However, with the continuous increase in the complexity of application scenarios, the inherent defects of classic artificial immune algorithms have become prominent. On this basis, based on the basic framework of artificial immune algorithms, this paper comprehensively analyzes the core key problems in algorithm operation and constructs a multi-dimensional and highly adaptive algorithm optimization system, aiming to provide comprehensive support for iterative optimization and scenario-based implementation of artificial immune algorithms.