MATLAB Implementation of Artificial Immune Algorithm

This document presents a comprehensive MATLAB implementation of the artificial immune algorithm (AIA). The implementation features a complete, ready-to-run codebase that includes key algorithmic components such as antibody initialization, affinity calculation, clone selection, and mutation operations. The code structure follows modular design principles, with separate functions handling antigen presentation, immune memory update, and population evolution processes. Artificial immune algorithm is a computational method inspired by biological immune systems, mimicking their characteristics and mechanisms to solve complex optimization problems. This implementation provides practical insights into AIA's core principles through well-commented code that demonstrates antigen-antibody interaction simulation, diversity maintenance techniques, and adaptive response mechanisms. Researchers can utilize this MATLAB platform to explore immune algorithm applications in pattern recognition, optimization problems, and anomaly detection scenarios. The implementation includes configurable parameters for population size, mutation rates, and selection thresholds, allowing users to customize the algorithm for specific problem domains. The codebase supports easy modification and extension, enabling researchers to incorporate additional immune-inspired operators or hybridize with other optimization techniques. This framework serves as an educational tool for understanding immune algorithm dynamics while providing a robust foundation for advanced research projects. Users can analyze algorithm performance through built-in visualization functions that track convergence behavior and population diversity metrics throughout the optimization process.