中文摘要：

多目标优化问题是进化算法领域的研究热点与难点.基于分解的多目标进化算法（MOEA/D）在求解多目标优化问题时有着较强的搜索能力、高效的适应度评价、良好的收敛性等优点.然而，不同的子问题使用相同大小的邻域统一优化，减缓算法搜索全局最优解的速率.为解决以上问题，提出一种动态邻域设置策略，针对不同的子问题设置不同的邻域.首先，分析子问题差异处理的原因；其次，根据子问题与边界的距离，提出边界子问题与靠边界子问题的邻域减小，其他子问题邻域增大策略并将以上策略应用在MOEA/D中，提出一种动态邻域的分解多目标进化算法，进一步分析改进算法中参数的敏感性.将该算法在经典测试函数ZDT系列，WFG系列上进行仿真实验，并采用反向世代距离（IGD）和超体积（HV）指标对算法性能对比分析.结果表明，与MOEA/D对比，改进算法的收敛性明显提高，求出的解集相比MOEA/D，NSGA-II，MOEA/D-DU同类典型的算法求出解集的质量更高，算法在求解前端为凸面的情况效果甚好。

英文摘要：

Multi-objective optimization problem is the hot and difficult research in the field of evolutionary algorithm.The multi-objective evolutionary algorithm based on decomposition （MOEA/D） owes strong search ability,efficient fitness,evolution,good convergence merits in solving multi-objective optimization problems.However,different sub problems use the same size of neighborhood for optimization,which slow down the rate of algorithm for searching the global optimal solution.Aiming at this problem,this paper proposes a dynamic neighborhood setting strategy,distinct neighbors are settled according to the different sub problems.First of all,anglicize the reason of not equally treating them; and then,propose the strategy of which decreasing the neighborhood size of the boundary sub problem and the nearest,but the others neighborhood size is increasing,according to the distance between the sub problems and boundary,at last,the strategy is adopted in the MOEA/D,proposing a new algorithm.Furthermore,the special arguments of proposed algorithm are analyzed.The performance of the new algorithm is evaluated in the classic problems such as ZDT,WFG and analyzing the property of the algorithm by Inverted generational distance and Hypervolume indicator,the results show that the convergence performance of the algorithm achieve greatly improvement,on the contrary of MOEA/D.And then,the performances of the solutions which are obtained by the improved algorithm are higher than solutions by the similar algorithms such as MOEA/D,NSGA-II,MOEA/D-DU,especially on the problem with convex surface.