关键词: 多目标优化, 动态ε支配, 基于动态ε支配的多目标遗传算法（DEMOEA）

Abstract: There are some limitations in MOEA based on Pareto dominance，such as it is easy to degraded and so on.Then the MOEA based on ε-dominance can solve these problems and it can make a preferable convergence and spread.But in the conventional ε-MOEA，the most difficult problem is the setting of ε and the loss of part of the extreme individuals is serious.In order to solve these problems，this paper proposes a new ε-MOEA based on dynamic ε（DEMOEA），it doesn’t need to set the ε by yourself and this paper imports a concept of dynamic grid to solve the loss of extreme individuals.Comparing with two other classical algorithms NSGA-II and SPEA2 in experiment，the result shows that the algorithm suggested in the paper（DEMOEA） gets improved convergence and diversity.

Key words: multiobjective optimization, dynamic ε-dominance, Multi Objective Evolutionary Algorithm Based on Dynamic（DEMOEA）