Abstract:A improved multi-objective ant colony optimization algorithm based on decomposition is presented. Tchebycheff Approach was firstly used to decompose the problem of approaching the Pareto optimality front into a number of single-objective optimization subproblems, and ant colony optimization algorithm is used for these subproblems. In order to make the solution set uniformly distributed along the Pareto front, a clustering method based on probe is used to classify the solution set, and the weight vector set in decomposition strategy was rearranged based on this solution set,with a consequence of the weight vector set adapted to a particular Pareto front; The ants are grouped according to the corresponding distance of the weight vector , An ant group maintains a pheromone matrix,which contains the location information of the sub Pareto front ; Each ant is responsible for solving one subproblem, an ant has a heuristic information matrix. Each ant has several neighboring ants. An ant updates its current solution if it has found a better one in its neighbors; To construct a new solution, an ant combines information from its group’s pheromone matrix, its own heuristic information matrix, and its current solution. Adaptive mutation operator is used to adjust the number of ants neighbors to improve the convergence speed and the quality of the solution of the algorithm. Experimental results for biobjective TSP show that the algorithm is more effective than other related algorithms.