MATLAB-Based Artificial Immune Algorithm Implementation

I am delighted to share this carefully developed MATLAB implementation of an artificial immune algorithm. The code incorporates key immune system principles including antigen recognition, antibody generation, and immune memory mechanisms through optimized MATLAB functions. The implementation features adaptive mutation operators, cloning selection processes, and population diversity maintenance algorithms. I welcome technical feedback and suggestions for improvement, particularly regarding algorithm efficiency and code optimization techniques. This collaborative platform aims to foster knowledge exchange and joint development opportunities in computational intelligence. I look forward to engaging discussions and collective advancement in immune-inspired computing methodologies. The code structure includes: - Main optimization function handling antibody initialization and antigen presentation - Affinity calculation module using distance-based similarity metrics - Clonal selection algorithm with proportional replication mechanisms - Hypermutation operators implementing Gaussian or Cauchy mutation strategies - Memory cell update functions for preserving optimal solutions - Convergence monitoring with visualization capabilities Key algorithmic features: - Parameterizable population size and mutation rates - Customizable objective functions for various optimization problems - Real-time performance tracking and result logging - Modular design allowing easy integration with other bio-inspired algorithms