Abstract: Aiming at the problem that the traditional methods for solving  high-dimensional multi-objective optimization problems had the defects of convergence and distribution uniformity of solution sets. We   proposed to design a novel high-dimensional multi-objective optimization algorithm based on the combination of global sorting method and  grey association analysis. By designing the parent sequence of minimum function values and the subsequence of individual objective function values, the grey association analysis method  was used to calculate  the association degree, and  combined with  the individual objective  fitness calculation strategy, the problem of uneven distribution of solution sets was solved. The algorithm could not only improve the selection ability of non-dominant individuals, but also had good convergence. In order to test the  performance of the algorithm, we chose three classical multi-objective evolution algorithms to carry out  comparative experiments on the standard test function set DTLZ {2,4,5,6}. Experimental results show that the proposed algorithm has better convergence and uniform distribution of solution set than  the other three algorithms in  solving the high-dimensional multi-objective problems.

Key words: multi-objective optimization, global sorting, grey association analysis, convergence and , distribution of solution set