NSGA-Ⅱ

Step1: Initialize the population, randomly generate a set of individuals that meet the fitness requirements as the initial population.

Step2: Non-dominated sorting, according to the objective function value of individuals, the population is non-dominated. The non-dominated ordering divides the individuals into different ranks, and the individuals in each rank are not dominated by the more superior individuals.

Step3: Crowding Calculation, In order to maintain the diversity of the population, NSGA-II uses the crowding distance to measure the distribution density of individuals in the target space. Crowding degree distance indicates the density of target vectors around an individual, and a higher crowding degree distance indicates a more dispersed individual.

Step4: Selection operation to select a certain number of individuals as parents based on non-dominated ordering and crowding degree distance.

Step5: Crossover and mutation, perform crossover and mutation operations on the selected parent individuals to generate new offspring individuals.

Step6: Update the population, merge the parent and child individuals, and perform non-dominated sorting and crowding degree calculation according to the population size.

Repeat Steps 2-6 until the specified stopping condition is reached (e.g., number of iterations or optimal solution reached).