Solving TSP Problem Using Genetic Algorithm with MATLAB Implementation

This article presents a genetic algorithm approach to solve the Traveling Salesman Problem (TSP), a classic combinatorial optimization challenge that involves finding the shortest possible route visiting each given city exactly once. The implementation uses MATLAB code featuring key genetic algorithm components: population initialization with random path permutations, fitness evaluation based on total route distance, tournament selection for parent chromosomes, ordered crossover (OX) for offspring generation, and swap mutation operators to maintain population diversity. The algorithm iteratively evolves solutions through generations, utilizing elitism to preserve best-performing individuals. The provided city coordinates enable validation of the algorithm's convergence and solution quality. This method demonstrates practical applications in travel routing, logistics planning, and network optimization. Genetic algorithms have proven effective for TSP by exploring diverse path combinations through bio-inspired operations, delivering high-quality solutions for complex routing scenarios.