Source Code for Solving TSP Using Information Entropy-Based Immune Algorithm

This algorithm provides the source code implementation for solving the Traveling Salesman Problem (TSP) using an information entropy-based immune algorithm. The algorithm is designed to find the optimal TSP route that visits each city exactly once while minimizing the total travel distance. The implementation utilizes information entropy concepts to improve search efficiency and solution accuracy by measuring population diversity during optimization. It incorporates immune algorithm principles that simulate biological immune system mechanisms for pathogen recognition and elimination, effectively applied here to eliminate poor solutions and preserve high-quality paths. Through the combination of information entropy metrics and immune algorithm operations, this approach achieves better TSP solutions by maintaining population diversity while accelerating convergence. Key algorithmic components include antigen recognition (problem initialization), antibody generation (solution creation), affinity calculation (fitness evaluation), and entropy-based diversity maintenance. The code structure features modular functions for mutation operations, clone selection, and entropy calculations that dynamically adjust the search strategy throughout the optimization process.