In this paper, we have proposed a modified model of dynamic pheromone updation for ant system, entitled as Dynamic Adaptive Ant System (DAAS) by incorporating the dynamic property in the pheromone trail factor with the help of Least Means Square (LMS) algorithm. Here, static pheromone trail factor, ρ, in ant learning equation, has been made dynamic and adaptive to increase the effectiveness of the algorithm and to resolve the basic shortcoming of easily falling into local optima and slow convergence speed. DAAS modifies its properties in accordance to the requirement of surrounding domain and for the betterment of its performance in dynamic environment. The experimental evaluation has been conducted to find out the usefulness of the new strategy, using selective benchmark problems from TSP library . Our algorithm shows effective and comparable results as compared to other existing approaches. © 2012 IEEE.