A novel pheromone initialization strategy of ACO algorithms for solving TSP

Travelling salesman problem (TSP), as a famous combinational optimization problem, has promoted the generation of a large number of algorithms. However, the existing algorithms, such as ant colony optimization (ACO) algorithms, still need to be enhanced further in terms of their robustness and the quality of the solution. In this paper, a novel pheromone initialization (NPI) strategy of ACO algorithms has been proposed for solving TSP, which shows a better efficiency in both robustness and the quality of the solution. Combining NPI strategy with a typical ACO algorithm like ant colony system (ACS) algorithm, a novel algorithm, called NPI-ACS algorithm, is put forward to strengthen the efficiency of ACS. Meanwhile, seven different scale datasets related to TSP are used to estimate the performance of NPI strategy. Afterwards, the experimental results show that there is a remarkable improvement in terms of robustness and the quality of the solution. Moreover, the proposed NPI strategy is flexible enough to be combined with multifarious ACO algorithms for solving TSP because of its independence in operation details.