Abstract: An adaptive ant colony algorithm(AACA)is established to solve the problems of long computing time and stagnation behavior of the basic ant colony optimization which is applied for concurrent design tasks planning and scheduling.A path selection mechanism is designed for the ant path diversity.The path pheromones is updated according to a dynamic factor of objective function values.Some ants are replaced at a dynamic rate with mutated ants,which leads to the evolution of the colony.Case simulation results show that the AACA has a excellent ability of global optimization and high search efficiency,and can settle the contradiction between convergence speed and stagnation behavior.