关键词: 多目标进化算法, 收敛性, 局部收敛, 归档集

Abstract: This paper proposes a Multi-Objective Evolutionary Algorithm Estimates（MOEAE） based on local convergence.In the evolutionary process of calculating for similarity between two generations archive set，if the algorithm in the early running for two generations of its similarity archive sets less than the pre-set threshold，then the algorithm has a certain probability that the local convergence.Then the authors re-initialize the probability of internal population and mutate some part of individuals in archive sets so that this algorithm may be in a local optimum，can produce new individual，thereby enhancing the convergence and diversity of solutions.By contrast experiment with the classic multi-objective algorithm，the experiment results show that the effectiveness of the algorithm.

Key words: Multi-Objective Evolutionary Algorithm（MOEA）, convergence, Local Convergence（LC）, archive set