Ant　colony　optimization　(ACO)　is　a　population-based　metaheuristic　technique　to　solve　combination　optimization　problems　effectively,　such　as　traveling　salesman　problem　(TSP),　multidimensional　knapsack　problem　(MKP),　and　so　on.　However,　how　to　improve　the　performance　of　ACO　algorithms　is　still　an　active　research　topic.　Though　there　are　many　algorithms　solving　TSPs　effectively,　there　is　an　application　bottleneck　that　the　ACO　algorithm　costs　too　much　time　in　order　to　get　an　optimal　solution.　Combining　the　pheromone　updating　with　an　improvement　of　the　stochastic　search　strategy,　a　fast　ACO　algorithm　for　solving　TSPs　is　presented　in　this　paper.　Firstly,　a　new　pheromone　increment　model　called　ant　constant,　which　keeps　energy　conversation　of　ants,　is　introduced　to　embody　the　pheromone　difference　of　different　candidate　paths.　Meanwhile,　a　pheromone　diffusion　model,　which　is　based　on　info　fountain　of　a　path,　is　established　to　reflect　the　strength　field　of　the　pheromone　diffusion　faithfully,　and　it　strengthens　the　collaboration　among　ants.　Finally,　a　simple　mutation　strategy　with　lower　computational　complexity　is　adopted　to　optimize　the　evolution　result.　Experimental　results　on　different　benchmark　data　sets　show　that　the　proposed　algorithm　can　not　only　get　better　optimal　solutions　but　also　enhance　greatly　the　convergence　speed.