Artificial Immune Algorithm: Optimization Technique Inspired by Biological Immunity

The Artificial Immune Algorithm (AIA) is an intelligent optimization technique inspired by biological immune systems, designed to solve both single-objective and multi-objective optimization problems. Its core concept mimics biological immune mechanisms including antibody production, antigen recognition, and immune memory to search for optimal solutions. In code implementation, this typically involves initializing a population of candidate solutions (antibodies) and iteratively evaluating their fitness against problem objectives (antigens).

The algorithm maps the solution space of optimization problems to antibody populations, while the objective function corresponds to antigens. During iterative processing, the algorithm preferentially retains high-affinity antibodies (quality solutions) and continuously improves solution quality through cloning selection, mutation, and memory mechanisms. For multi-objective optimization, the algorithm can incorporate diversity maintenance strategies to ensure balanced distribution of solution sets across multiple objectives. Key implementation steps include calculating antibody affinity using fitness functions, performing clonal expansion based on affinity values, and applying mutation operators to maintain population diversity.

Compared to traditional optimization methods, the Artificial Immune Algorithm demonstrates strong global search capabilities and high adaptability, making it particularly suitable for handling complex optimization scenarios involving non-linear functions and multi-modal landscapes. Code implementations often feature adaptive parameter tuning and archive mechanisms for preserving non-dominated solutions in multi-objective cases.