Advances in International Computer Science

As a classic combinatorial optimization problem, the Traveling Salesman Problem (TSP) is widely applied in fields such as path planning and logistics scheduling. Traditional algorithms exhibit low efficiency in solving large-scale TSP, making it difficult to meet practical needs. This paper focuses on the application of genetic algorithms in TSP, proposing a solution strategy based on improved genetic operators. By optimizing crossover and mutation mechanisms and introducing a local search strategy, the algorithm's convergence speed and solution quality are enhanced. Experimental results show that this method achieves superior path lengths in multiple standard test datasets, verifying its effectiveness and stability. It provides new solutions for complex optimization problems.

Genetic algorithm; Traveling Salesman Problem (TSP); Path optimization; Crossover and mutation; Combinatorial optimization