中文说明： NSGA-II算法提出了快速非支配排序法，降低了算法的计算复杂度。由原来的O(MN3)降到O(MN2)（M为目标函数个数，N为种群大小）。提出了拥挤度和拥挤度比较算子，代替了需要指定共享半径的适应度共享策略，并在快速排序后的同级比较中作为胜出标准，使准Pareto域中的个体能扩展到整个Pareto域，并均匀分布，保持了种群的多样性。引入精英策略，扩大采样空间。将父代种群与其产生的子代种群组合，共同竞争产生下一代种群，有利于保持父代中的优良个体进入下一代，并通过对种群中所有个体的分层存放，使得最佳个体不会丢失，迅速提高种群水平。
English Description：
NSGA-II algorithm proposes a fast non dominated sorting method, which reduces the computational complexity of the algorithm. From O (Mn3) to o (Mn2) (M is the number of objective function, n is the population size). The crowding degree and crowding degree comparison operators are proposed to replace the fitness sharing strategy which needs to specify the sharing radius. The crowding degree and crowding degree comparison operators are used as the winning criteria in the peer comparison after quick sorting, so that the individuals in the quasi Pareto domain can be extended to the whole Pareto domain and evenly distributed, maintaining the diversity of the population. The elite strategy is introduced to expand the sampling space. Combining the parent population with the offspring population to compete together to produce the next generation population is beneficial to keep the excellent individuals in the parent generation into the next generation, and through the hierarchical storage of all individuals in the population, the best individuals will not be lost and the population level will be improved rapidly.