TSP Algorithm Based on Genetic Optimization

This document presents a MATLAB m-file implementation of a Traveling Salesman Problem (TSP) solver utilizing genetic algorithm methodology. The code has been rigorously tested and demonstrates reliable performance with optimal results. The genetic algorithm approach mimics natural evolutionary processes to efficiently solve TSP by maintaining a population of candidate solutions, applying selection, crossover, and mutation operations to evolve toward optimal routes. Key implementation features include chromosome encoding of city sequences, fitness evaluation based on total path distance, tournament selection mechanism, ordered crossover for route preservation, and swap mutation operators. The algorithm efficiently handles TSP instances of varying scales through configurable parameters such as population size, generation count, and mutation rate. The modular code structure allows straightforward customization and optimization to address specific problem constraints or performance requirements. This implementation provides a robust foundation for solving route optimization challenges while maintaining extensibility for algorithm enhancements.