MATLAB Implementation of Artificial Immune Algorithm with Code Explanation

Artificial Immune Algorithm is a bio-inspired intelligent algorithm designed based on biological immune systems. It simulates the learning, memory, and pattern recognition mechanisms of biological immunity, widely applied in optimization, pattern recognition, and anomaly detection fields. Implementing Artificial Immune Algorithm in MATLAB typically involves the following core steps with code-specific considerations: The initialization phase requires randomly generating an initial antibody population of specified size, where antibodies represent potential solutions. Each antibody is commonly represented as a vector or matrix using MATLAB's array operations, corresponding to points in the solution space of the optimization problem. Affinity calculation forms the algorithm's critical component. Developers need to design a fitness function (e.g., using function handles or separate .m files) to evaluate each antibody's quality, representing the solution's effectiveness. High-affinity antibodies are preserved while low-affinity ones may be eliminated through logical indexing operations. The cloning selection process mimics cellular proliferation in immune responses. High-affinity antibodies are selected (using sort() or max() functions) and undergo clonal expansion through vectorized operations, generating numerous similar copies. These clones then undergo mutation operations implemented using random number generators (rand(), randn()) to enhance population diversity. Mutation operations typically employ random perturbation strategies such as uniform mutation (using rand()), Gaussian mutation (using randn()), or other custom mutation approaches. Mutation magnitude requires careful parameter tuning via scaling factors - excessive values may cause instability while insufficient values reduce exploration capability. Population update strategies involve competition between newly generated and existing antibodies. Selection mechanisms like roulette wheel selection (using cumsum() and find()) or tournament selection maintain population size and diversity. Memory cells can be implemented through persistent variables or global arrays. Complete implementation requires termination condition checks, including maximum iterations (for-loop counters), convergence thresholds (difference calculations), or time limits (tic/toc functions). Upon algorithm completion, the highest-affinity antibody is selected as the optimal solution using max() operations. MATLAB's implementation leverages matrix computation advantages for efficient parallel processing of antibody populations through vectorized operations. Proper parameter optimization using sensitivity analysis can achieve excellent optimization performance with clear visualization plots showing convergence curves.