| Researchers have more and more paid attention to the multi-objective optimization problems from 1960s. We can conclude that EMO methodologies may provide an advantage over their classical counterparts, such as classical research and optimal methods through the research on Evolutionary Algorithms recently. EMO algorithms have been applied to various problems and attached importance to the researchers in decision and optimal fields.This paper analyzes the foundations of the current popular MOEAs and the basic idea of various methods of MOEAs in general. Demonstrating the weak location of multi-objective optimization field is high-dimension Evolutionary multi-objective optimization algorithms with preference information and the measure of the performance of multi-objective optimization algorithms, so that we ascertain the research method and goal of this paper. The work and creative point of this paper are described as follows:1,Presenting the reference point based evolutionary algorithm (namely MR-NSGA-II algorithm). High-dimension multi-objective optimization problems are solved by NSGA-II algorithm with the new mutation operator and preference operator. Time complexity and the performance of the algorithm are analyzed in general. The effectiveness of the algorithm is tested by the use of high-dimension test functions.2,The measure of the performance of multi-objective optimization algorithms includes test functions and metrics. Analyzing the construction methods of multi-objective optimization test functions. Proving the effect of the construction methods in theory. Analyzing three metrics used in measuring performance of multi-objective optimization algorithms, we presenting three new metrics RER,RSP and RGD to measuring the obtained preferred sets.At last, the algorithm and performance metrics are conclude and some of the most promising future paths of research in multi-objective optimization area are also addressed.
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