Abstract: In the real-world applications，multi-objectives optimization involve a large number of objective，however，existing algorithms are only efficient to the problems with no more than three objectives.Because the inability to be represented in the Euclidean space as it is more than 3 objectives，and the high computation complexity，researchers fall to doing with how to transform large objectives to the smaller ones.In this paper，some existed algorithms on transforming high-dimensional to low-dimensional are introduced，and then a new algorithm is proposed.This method fits every objective function to a line，and compares the slope differences between each two lines，finally makes certain which one is redundancy and further reduces this one.

Key words: multi-objective optimization, reduce redundancy objective, data fitting

摘要： 在多目标优化中，许多实际问题都是由很多目标（超过三个）所组成，但是目前提出的大多数算法却只有在三维以下时高效。由于超过三维的情况无法用欧式空间来表示，而且在处理高维问题时，算法的时间复杂度通常很高，因此人们开始考虑将高维目标转化为低维目标后再处理。首先介绍了目前已经存在的将高维目标转化为低维目标的算法，提出了一种新的算法，该方法通过数据拟合，将各目标函数拟合为一条直线，比较相互之间的斜率之差来确定目标是否存在冗余，以期减少冗余目标。

关键词: 多目标优化, 冗余目标减少, 数据拟合