Ant colony optimization for traveling salesman problem based on parameters optimization

Abstract

Traveling salesman problem (TSP) is one typical combinatorial optimization problem. Ant colony optimization (ACO) is useful for solving discrete optimization problems whereas the performance of ACO depends on the values of parameters. The hybrid symbiotic organisms search (SOS) and ACO algorithm (SOS–ACO) is proposed for TSP. After certain parameters of ACO are assigned, the remaining parameters can be adaptively optimized by SOS. Using the optimized parameters, ACO finds the optimal or near-optimal solution and the complexity for assigning ACO parameters is greatly reduced. In addition, one simple local optimization strategy is incorporated into SOS–ACO for improving the convergence rate and solution quality. SOS–ACO is tested with different TSP instances in TSPLIB. The best solutions are within 2.33% of the known optimal solution. Compared with some of the previous algorithms, SOS–ACO finds the better solutions under the same preconditions. Finally, the performance of SOS–ACO is analyzed according to the changes of some ACO parameters. The experimental results illustrate that SOS–ACO has good adaptive ability to various values of these parameters for finding the competitive solutions.