Solving the Traveling Salesman Problem for Efficient Route Planning Through Swarm Intelligence Based Optimization

The Traveling Salesman Problem (TSP) has long been a challenging optimization puzzle, prompting the development of various methodologies to seek efficient solutions. In this paper, we propose an improved method utilizing a Swarm Intelligence-based approach. Besides the traditional MIX and MOVE operations, our major contribution lies in the introduction of a new MIX operation specifically tailored for the TSP. This new operation offers an advantage over the traditional MIX operation by providing more comprehensive domain exploration. We compare our improved method to conventional optimization techniques, namely the Genetic Algorithm and Ant Colony Optimization. For a TSP involving 50 popular U.S. landmarks, our proposed method outperforms the others in terms of solution quality and computational time. Results are visualized on maps for reference.