As one of the most challenging problems in combinatorial optimization, the traveling salesman problem has attracted extensive attention from the academic community since its birth, and a large number of methods have been proposed to solve it. The ant colony optimization (ACO) is a heuristic bionic evolutionary algorithm for solving complex combinatorial optimization problems, which is effective in solving the traveling salesman problem. This study introduces several representative ACOs and makes a literature review of the improvement, fusion, and application progress of ACOs to evaluate the development and research achievements of different versions of ACOs in solving the traveling salesman problem in recent years. Moreover, the improved ACOs are summarized in categories in terms of the framework structure, setting and optimization of algorithm parameters, pheromone optimization, and hybrid algorithms. The research provides an outlook and basis for the ACO application to solve the traveling salesman problem and further develop the research content and focuses of other fields.