A Collection of Artificial Immune Algorithm Programs

Artificial Immune Algorithm is a computational intelligence method inspired by biological immune systems, widely used in optimization, pattern recognition, and machine learning. This collection of programs provides multiple MATLAB implementations of immune algorithms, offering valuable reference resources for researchers.

The core concept of artificial immune algorithms involves simulating antibody generation, selection, and adaptation mechanisms from biological immune systems. Representative algorithms include Negative Selection Algorithm, Clonal Selection Algorithm, and Immune Network Algorithm. MATLAB implementations typically involve key steps such as population initialization, affinity calculation, clonal expansion, and mutation operations.

MATLAB's powerful matrix operations and visualization capabilities make it particularly suitable for implementing these algorithms. Researchers can quickly understand algorithmic details through these programs and proceed with improvements or applications. For instance, a clonal selection algorithm implementation might include: randomly generating initial antibody populations, calculating antibody-antigen affinity, selecting high-affinity antibodies for cloning and mutation, and ultimately optimizing solution quality through iterative refinement.

This resource package is highly beneficial for immune computing research, serving not only for algorithm learning but also as a foundation for developing new algorithms or solving practical optimization problems. For researchers, understanding and adjusting code parameters (such as population size and mutation rate) is crucial for mastering artificial immune algorithms. The implementations demonstrate practical coding techniques including vectorized operations for affinity calculations, dynamic population management, and adaptive mutation strategies that enhance algorithm performance.