中文摘要：

在Evo-Ant算法的基础上提出了多目标的算法,即利用Evo-Ant算法来产生新的解,并利用一个额外的存储空间来存放Pareto候选解,用新产生的解来更新Pareto候选解,消除被支配的解,依次循环,从而得到近似的Pareto解.为了验证演化蚁群算法,采用2种测试手段：一种是Solomon的测试数据;另一种是在仿真环境下的测试.实验结果表明该算法很具有竞争能力.

英文摘要：

A new MOPs algorithm, named MEvo-Ant（multi-objective evolutionary ant algorithm）, is proposed for the DVRPs（dynamic vehicle routing problem ）. In the MEvo-Ant algorithm an archive is used to storage the candidate Pareto solutions while the Evo-Ant algorithm is employed to generate new solutions and update the archive to eliminate the dominated solutions. With the iterations the solutions in the candidate Pareto set are approach to the true Pareto solutions. In this paper two methods are used to evaluate the MEvo-Ant, the one is used the Solomon testing data, and the other is used a simulator. The experiment results show that the MEvo-Ant algorithm is effective to the DVRPs.