The traveling salesman problem (TSP) is one of the most well-known combinatorial optimization problem and holds a central place in logistics management. This problem has received much attention because of its practical applications in industrial problems and many exact, heuristic and metaheuristic approaches have been proposed to solve TSP. Recently, many researchers have found that the employment of hybridization in optimization problems can improve the quality of obtained solutions in comparison with heuristic and meta-heuristic approaches. Since hybrid algorithms have greater ability to find an optimal solution, they have been considered by researchers and scientists in recent years.Besides, since the TSP is an NP-hard combinatorial optimization problem, metaheuristic methods have proved to be more suitable for dealing with such problem in terms of solution quality and computational cost. So,a modified ant colony optimization is presented in this paper.The proposed algorithm uses only a global updating, which will increase pheromone on the edges of the best routes. Furthermore, a candidate list and some local search algorithms are combined for obtaining high-quality solutions. It can be concluded that making use of some proposed modifications leads the algorithm avoids risky edges and moves toward more appropriate edges and finds better solutions as a result.The proposed metaheuristic algorithm is tested on the well-known TSP instances involving 14 benchmark problems from 48 to 200 nodes. The computational results show that our algorithm is better than other meta-heuristic algorithms in terms of solution quality. Furthermore, the gap of the proposed algorithm stays on average almost 1% of the execution time and also the most best known solutions of the benchmark problems are foundby the proposed algorithm.
