Traveling Salesman Problem (TSP) is a challenging combinatorial optimization problem that determines the shortest closed tour that visits a set of cities exactly once and returns to the starting point. TSP has been a subject of interest for researchers due to its complexity and the quest for the best solution. Coati Optimization Algorithm (COA) is a recently developed population-based metaheuristic inspired by the behavior of coatis in their natural environment, which has proven effective in solving continuous optimization problems. This paper introduces a novel approach to tackle TSP by adapting COA into a discrete version called Discrete Coati Optimization (D-COA), incorporating the 2-opt algorithm into its framework. The proposed Discrete Coati Optimizer aims to efficiently explore the TSP solution space, iteratively refining the solution to find the optimal outcome. The proposed D-COA algorithm is tested on 10 benchmark functions for TSP. Its performance is compared with three classical discrete optimization algorithms: Discrete Bat Optimization Algorithm, Discrete Symbiotic Organism Search, and Discrete Tree Seed Algorithm. The results are validated using standard deviation and average parameters. After extensive experiments and comparisons, the results reveal that the coati-inspired optimization algorithm is efficient in solving combinatorial optimization problems.