The main purpose of this dissertation is to introduce two new advanced methods of solving a Traveling salesman problem (TSP) using metaheuristics. Solving TSP is important as it is an NP-hard and can’t be solved in a polynomial time. In my new methods, I applied k-means to cluster the data into smaller parts and I used the Whale Optimization algorithm (WOA) as a bio-inspired algorithm. I merged TSP with K-means and WOA, and I assigned a number for thresholding (T- value) to decide the maximum number of cities that can be placed in each cluster. This way the fitness function and timing of solving TSP improved. The two methods have close pseudocodes. There is a third model as well that is proposed for future works.