Programming of Artificial Immune Genetic Algorithm Based on MATLAB

I have developed a MATLAB-based implementation of an artificial immune genetic algorithm, presented below with comprehensive annotations. This programming solution aims to support researchers and engineers in tackling complex optimization challenges. This implementation utilizes MATLAB programming language to realize an artificial immune genetic algorithm, which integrates immune system learning mechanisms with evolutionary processes to demonstrate exceptional performance in optimization problem-solving. The programming begins by importing necessary MATLAB libraries and functions, typically including global optimization toolbox components and custom utility functions. Next, we define the problem-specific objective function and constraint conditions using MATLAB function handles or separate m-files. The algorithm initialization phase involves creating an initial population, typically through random generation using MATLAB's rand or randn functions, while setting key parameters such as iteration count (generations), population size, crossover rate, mutation probability, and immune response thresholds. During each iteration, the program calculates individual fitness values using the defined objective function, followed by selection operations (using techniques like roulette wheel or tournament selection), crossover operations (implementing single-point or multi-point crossover), and mutation operations (applying Gaussian or uniform mutation). These evolutionary mechanisms continuously refine population individuals through MATLAB matrix operations and logical indexing. Key immune algorithm components include antibody diversity maintenance through concentration calculations and vaccination operations, implemented using MATLAB's array manipulation capabilities. The program incorporates memory cell mechanisms to preserve high-quality solutions. After completing the programming, users can test algorithm performance by executing the main script, with results visualized through MATLAB's plotting functions. Performance metrics like convergence curves and solution quality can be analyzed for further algorithm tuning and improvement. This MATLAB implementation provides a practical framework for applying artificial immune genetic algorithms to various optimization problems, offering both educational value and research applicability.