Immune Clonal Algorithm for Function Optimization

This program implements function optimization using the immune clonal algorithm through MATLAB programming. The immune clonal algorithm represents a novel optimization approach that has attracted substantial research interest in recent years. Compared to traditional optimization algorithms, it exhibits superior convergence speed and enhanced global search capabilities. The implementation includes several critical components: antibody population initialization using random or heuristic methods, affinity calculation based on objective function evaluation, clonal selection proportional to antibody affinity, and mutation operations to maintain population diversity. The algorithm iteratively improves solution quality through these biological-inspired mechanisms. This program serves dual purposes: it provides a clear understanding of the immune clonal algorithm's implementation process while offering practical applications in real-world problems such as image recognition, signal processing, and complex system optimization. The code structure follows modular design principles, with separate functions for initialization, cloning, mutation, and selection phases, making it easily adaptable for various optimization scenarios. We anticipate this implementation will serve as a valuable reference for both research and practical applications of immune clonal algorithms, particularly for engineers and researchers working on complex optimization problems requiring robust global search capabilities.