| | Input: objective function, constraint function, decision variable and algorithm parameter, migration algebra T. |
| | Output: leading edge collection. |
| (1) | NSGA-II initialization. In this step, the algorithm parameters are obtained to generate the initial population required by the algorithm operation. |
| (2) | The initial population is parted into n subpopulations. |
| (3) | The population was ranked nondominated and the crowding degree was calculated. |
| (4) | Carry out operations on the individuals. |
| (5) | The next generation population is obtained from the intermediate population combination of 3 and 4 steps, and the individuals are sorted according to the fitness value. The fixed population size and number of individuals are retained, and the redundant individuals are discarded. |
| (6) | The migration operator is used for individual migration among subpopulations; if it is not a multiple of T, go to step 7. |
| (7) | Judge whether the termination conditions of NSGA-II are met. If yes, go to step 8; otherwise, go to step 3. |
| (8) | Determine the population size, dimensions, values in each dimension, the initial position and velocity of particles, and the upper and lower limit values of the position and velocity of particles in each dimension. |
| (9) | The leading edge strategy is used as the initial particle swarm optimization, and the redundant particles are discarded according to the results of fitness ranking and congestion ranking. |
| (10) | Perform nondominated sorting, record the individual top location (ITP), group top location (GTP), and update the particle location and speed. |
| (11) | Judge the number of iterations and decide whether to end the algorithm. If the algorithm termination conditions are met, the optimal set will be nondominated sorted and the leading edge set will be output |
