Application of improved ant colony optimization algorithm on traveling salesman problem

Traveling salesman problem (TSP) is a typical combinatorial optimization problem and a NP problem in operations research. Ant colony algorithm (ACO) is a kind of probability technology used to find the optimal path in the graph. Through the analysis on the main reasons resulting in the premature stagnation phenomenon of standard ACO, the updating strategy of information hormone is modified, and the changing parameters and local optimal search strategy are introduced to effectively restrain the premature stagnation phenomenon in the convergence process. Then the improved ant colony algorithm is applied to solve the typical TSP problem. The simulation experiments results show that the improved ACO is effective for solving traveling salesman problem, which not only accelerates the convergence velocity, but also inhibits the premature stagnation in the convergence process.