Comparative Study of Artificial Immune Algorithm and Genetic Algorithm for TSP Problem Research

This study involves MATLAB programming to implement and compare Artificial Immune Algorithm and Genetic Algorithm approaches for solving the Traveling Salesman Problem (TSP). The implementation includes MATLAB code development for both algorithms with detailed performance comparison metrics. The research focuses on developing MATLAB programs that implement both Artificial Immune Algorithm (featuring immune system-inspired operators like cloning, mutation, and selection) and Genetic Algorithm (utilizing genetic operators including crossover, mutation, and selection). These algorithms are applied to solve the Traveling Salesman Problem, a classic combinatorial optimization challenge that involves finding the shortest possible route visiting each city exactly once and returning to the origin city. The MATLAB implementation involves creating separate function files for each algorithm, with key components such as population initialization, fitness evaluation, and optimization operators. The code includes parameter configuration for population size, mutation rates, and termination criteria. Different TSP datasets are used to test algorithm performance through multiple iterations, measuring solution quality and computational efficiency. By comparing the results from both Artificial Immune Algorithm and Genetic Algorithm implementations, we evaluate their effectiveness in solving TSP problems based on solution accuracy, convergence speed, and computational requirements. Performance metrics include optimal route length, computation time, and algorithm stability across different problem instances. The findings from this comparative study provide insights into algorithm selection and parameter optimization for TSP solutions, contributing to better understanding of these metaheuristic approaches. The research outcomes support the application of these algorithms to similar combinatorial optimization problems in various domains, while the MATLAB code structure serves as a template for future algorithm development and optimization studies.