An improved ant colony algorithm (ACA) is put forward to solve the mobile robot odor localization (MROL) problem. The so-called MROL means localizing an odor source with mobile robots. The improved algorithm is realized through three phases, which are genetic algorithm (GA) based local search, global search and pheromone update. The GA ensures that the optimal or sub-optimal points can be found within local areas. The global search phase consists of random and probability based searches. The random search can prevent the ACA from getting into local optimum. Detailed implementation procedure of the improved ACA for the MROL is presented. Two Gaussian concentration models are used to describe the odor distribution. Simulation results show that the robots can asymptotically approach and finally determine the odor source.