中文摘要：

由于追求收敛速度与防止陷入局部最优，标准的改进强度Pareto算法（SPEA2）过于注重全局搜索能力，从而导致局部搜索能力不足。为了增强SPEA2算法的局部搜索性能，进而提高算法收敛速度，提出了一种基于局部搜索的改进SPEA2算法。该算法单独设置一个新外部存档集以保存局部搜索后的非支配集，并且改进了交叉算子，加入了部分个体更新策略。将该改进算法与SPEA2算法进行了收敛性能比较实验。仿真实验结果表明，相比于标准算法，改进SPEA2算法不仅可以保证收敛到多目标优化问题的Pareto最优边界，而且在收敛能力上也得到了较好的改善。

英文摘要：

For reaching fast convergence rate and avoiding local optimum, the SPEA2 focus on to the global search capability while pay less attention to the local search ability. In order to overcome this poor local search ability of SPEA2, this paper presented an improved SPEA2 algorithm based on local search. The algorithm introduced an special external set for local searching, while maintaining the strong global search capability, and improved the crossover operator and introduced the part individuals replacement strategy. Finally, it carried simiulation on the improved algorithm and the SPEA2. The experimental results show that the proposed algorithm can improve the optimizing convergence capability, and reach the Pareto-optimal boundary.