Abstract: In order to improve the performance of the ant colony optimization algorithm, a new ant colony optimization algorithm with estimation of distribution (ACO-ED)was presented. ACO-ED uses probabilistic model based on estimating the distribution of promising solutions in the search space,and adjusts the state transition rule and the global updating rule. Furthermore,ACO-ED is significantly improved by extending with a local search procedure. We applied ACO-ED to TSP problems and compared it with other ant colony optimization algorithms. Simulation results show that ACO-ED is an effective and efficient way to solve combinatorial optimization problems.

Key words: Ant colony optimization, Estimation of distribution, TSP problem, Combinatorial optimization problem