中文摘要：

提出一种基于分解的、改进的多目标蚁群算法。该算法首先利用Tchebycheff聚合方法将整个Pareto最优前沿的逼近问题分解为一定数量的单目标优化子问题,然后利用蚁群算法同时求解这些子问题。为使解集均匀分布在Pareto前沿,采用基于试探的聚类方法对解集聚类;依据解集的分布重置分解策略中的权重向量集,使其适配于特定的Pareto前沿;蚂蚁按照对应的权重距离被分组,同一组蚂蚁共享一个信息素矩阵,该矩阵容纳学习到Pareto前沿子区域的位置信息;每个蚂蚁求解一个子问题,每个蚂蚁拥有自己的启发式信息矩阵;每个蚂蚁拥有多个邻居,蚂蚁选取邻居中的最优解来更新当前解;蚂蚁依据小组信息素,当前解和启发式信息构建新的解。引入自适应变异算子,动态调整蚂蚁邻居的个数,提高算法的收敛速度和解的质量。将该算法与其他相关算法在标准的双旅行商问题进行性能对比,证明该算法有效。

英文摘要：

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.