Practical MATLAB Source Code Implementation of Immune Algorithm

Immune Algorithm is an intelligent optimization algorithm that mimics the mechanisms of biological immune systems, demonstrating excellent global search capabilities and adaptability. The MATLAB-implemented source code for immune algorithm is particularly practical and easy to understand due to its concise and efficient characteristics.

The core concept of immune algorithm draws inspiration from biological immune processes including antigen recognition, antibody cloning, and memory mechanisms. By simulating these processes, the algorithm solves optimization problems through key implementation steps: initializing antibody population, evaluating antibodies (fitness calculation), generating new antibodies through cloning selection and mutation operations, and updating the antibody population to gradually approach the optimal solution. In MATLAB implementation, the antibody population is typically represented as a matrix where each row corresponds to an antibody, and fitness evaluation is performed using vectorized operations for efficiency.

MATLAB serves as an ideal platform for implementing immune algorithms due to its powerful scientific computing capabilities. Its rich matrix manipulation functions simplify algorithm coding through operations like population initialization using rand() or randn() functions, while visualization tools enable clear tracking of optimization progress. The algorithm's strength lies in effectively balancing global and local search, preventing premature convergence, making it suitable for complex multimodal function optimization problems. MATLAB's plotting functions can graphically demonstrate the algorithm's convergence behavior and population diversity.

For researchers and engineers, MATLAB-based immune algorithm source code provides excellent reference material for both learning and practical applications, enabling rapid deployment to real-world problems. The algorithm's performance significantly depends on parameter configuration (such as population size, mutation rate, and cloning factor) and fitness function design, which require problem-specific adjustments. The implementation typically involves creating main functions for algorithm flow control and separate functions for key operations like clone selection using repmat() and mutation via Gaussian noise addition.