MATLAB Implementation of Artificial Immune Algorithm with Code Descriptions

This article discusses the application of Artificial Immune Algorithm (AIA). AIA is a computational method inspired by biological immune systems that utilizes information entropy to calculate antibody diversity and enhances antibody affinity through clone selection and hypermutation processes. This algorithm simulates immune system mechanisms and can be effectively applied to problem-solving and optimization tasks. From an implementation perspective, the algorithm typically involves calculating antibody diversity using entropy-based metrics to maintain population diversity, followed by clone selection where high-affinity antibodies are replicated proportionally to their quality scores. The hypermutation phase then introduces controlled variations to explore the solution space more effectively. Key MATLAB functions that could be employed include entropy calculations for diversity measurement, sorting algorithms for affinity ranking, and mutation operators for solution refinement. By implementing this algorithm, we can discover superior solutions for complex problems, thereby enhancing system performance and optimization efficiency. The algorithm's strength lies in its balance between exploration (through diversity maintenance) and exploitation (via affinity maturation), making it particularly suitable for multi-modal optimization problems.