MATLAB-Based Artificial Immune Algorithm for Optimization

This MATLAB-based artificial immune algorithm implements an immune network model to solve optimization problems for function f(x1, x2). The algorithm mimics the working principles of the human immune system to address optimization challenges. It employs interactions between immune cells and signal transmission mechanisms to search for optimal solutions. The implementation utilizes key immune system components including: - Antibody generation and diversity maintenance through clonal selection - Antigen-antibody affinity calculations for solution evaluation - Immune network interactions that enable collaborative search in multi-dimensional spaces By incorporating the immune network model, the algorithm effectively handles multi-dimensional optimization problems and delivers more accurate results. The MATLAB implementation includes functions for: - Population initialization with random antibody generation - Affinity maturation through mutation and crossover operations - Memory cell maintenance for preserving high-quality solutions - Dynamic parameter adjustment based on convergence metrics This algorithm finds applications across various domains including engineering optimization, data mining, and machine learning. It addresses complex optimization problems by improving both solution efficiency and accuracy through biological-inspired computation mechanisms. The code structure allows for easy customization of objective functions and optimization parameters to adapt to different problem specifications.