Abstract: The main idea and the key point of LebMeasure algorithm, HSO algorithm, HSO+MWW algorithm and HKMP algorithm are introduced. The performance of the algorithms is compared on the different data set. Experimental results show that the performance of HSO+MWW is better than that of HSO for all of the fronts. For HKMP and HSO, it consists with the theory that the performance of the former is better than that of the latter when the number of the processed point exceeds some value. For the random front and the discontinuous front, the performance of HSO+MWW is worse than that of HKMP when the number of the processed point exceeds some value. But for the spherical front and the degenerate front, the performance of HSO+MWW is better than that of HKMP.

Key words: evolutionary computation, mulitiobjective evolutionary algorithm, hypervolume

摘要： 对LebMeasure算法、HSO算法、HSO+MWW算法以及HKMP算法的基本思路、关键问题进行评述，在几种测试数据集上对算法的性能进行比较验证。实验结果表明，对于所有类型的前沿，HSO+MWW的性能好于HSO算法；当处理点的数目超过某一值时，HKMP算法的性能好于HSO算法，与理论分析一致；对于HKMP算法和HSO+MWW算法，在random和discontinuous前沿上，当处理点的数目超过某一值时，HKMP算法的性能好于HSO+MWW算法；但在spherical和degenerate前沿上，HSO+MWW算法的实际性能远好于HKMP算法。

关键词: 进化计算, 多目标进化算法, 超体积

CLC Number: 

• TP301.6