Artificial Immune Algorithm

The Artificial Immune Algorithm is an intelligent optimization technique inspired by biological immune systems, simulating mechanisms such as antibody recognition, clonal selection, and mutation to solve complex optimization problems. This algorithm is particularly suitable for multimodal function optimization, pattern recognition, and anomaly detection applications.

When implementing the Artificial Immune Algorithm in MATLAB, key steps typically include: initializing the antibody population, which corresponds to generating random initial solutions within the search space; calculating antibody affinity, representing the evaluation of current solution quality; performing clonal selection where high-quality antibodies are selected for cloning and expansion; and conducting antibody mutation operations that introduce random perturbations to enhance global search capability.

A standard implementation incorporates modules such as affinity calculation functions, cloning operation functions, and mutation operation functions. The algorithm iteratively executes the selection-cloning-mutation process to progressively improve solution quality. MATLAB's matrix operations can efficiently implement these procedures through batch affinity calculations and parallel execution of cloning and mutation operations.

This algorithm excels at solving nonlinear, multimodal problems that challenge traditional optimization methods. By simulating the immune system's dynamic平衡 mechanisms, it maintains effective exploration-exploitation balance and prevents premature convergence.