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.

旅行商问题（TSP）是一种典型的组合优化问题。蚁群优化（ACO）可用于解决离散优化问题，而ACO的性能取决于参数值。针对TSP提出了混合共生生物搜索（SOS）和ACO算法（SOS-ACO）。在分配了ACO的某些参数后，其余参数可以通过SOS进行自适应优化。使用优化的参数，ACO可以找到最佳或接近最优的解决方案，并且大大降低了分配ACO参数的复杂度。另外，一种简单的局部优化策略被并入SOS-ACO中，以提高收敛速度和解决方案质量。SOS-ACO已在TSPLIB中使用不同的TSP实例进行了测试。最佳解决方案在已知最佳解决方案的2.33％之内。与以前的某些算法相比，SOS-ACO在相同的先决条件下找到了更好的解决方案。最后，根据一些ACO参数的变化，分析了SOS-ACO的性能。实验结果表明，SOS-ACO对这些参数的各个值具有良好的自适应能力，可以找到具有竞争力的解决方案。