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中文摘要：
分布性保持是多目标进化算法主要目标之一．然而通常维护方法的性能与运行时间存在矛盾．提出一种基于最小生成树的分布性维护方法．利用最小生成树中的度数和边长对个体密度进行估计，使低度数的边界个体和长边长的低密度个体得到了保留．另外，一次性选择个体进入下代种群，避免了每移出一个个体就需要对个体密度进行调整的操作．通过5个测试问题和4个方面的测试标准，与3个著名的算法进行比较实验，结果表明该方法在以较快速度对种群进行维护的同时，拥有良好的分布性．
英文摘要：
Maintaining the diversity of solutions is a crucial part in multi-objective optimization. However, there has to be a trade-off between the diversity and the execution time. A method for maintaining the diversity using a minimum spanning tree is proposed. By estimating the individual density based on the degree and edge of the minimum spanning tree, we preserve the low-degree boundary individuals and the longer-edge-low-density individuals. Moreover, by this one- time selection, the adjustment of individual density after removing each individual can be avoided. Through the extensive comparison study with three other classical methods on four performance metrics in five test problems, it is observed that the proposed method has a good performance in diversity and execution time.
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