| Input: Task Objects, Search Space (SP), total number of levels (), : Population size, : number of generations, |
| : number of objectives, MP: mutation probability, CP: crossover probability. |
| Output: Pareto optimal solutions, . |
| Processing |
| Step 1. Generate random initial population, IP. |
| (1.1) Randomly generate integer chromosome . |
| (1.2) Calculate the fitness of each random chromosome |
| (1.3) Rank population according to non-domination |
| Step 2. Evolve population . |
| (2.1) Select parents using tournament selection, |
| (2.2) Perform two-point dynamic crossover operation on parents with probability CP to produce |
| offspring population. |
| (2.3) Perform mutation operation with probability MP on random points applied to offspring |
| (2.4) Calculate fitness of new offspring population and update population |
| (2.5) Use non-dominated sorting to divide into several non-domination levels . |
| (2.6) Calculate Crowding Distance of all solutions |
| (2.7) Identify the worst solution and set . |
| Step 3. Repeat Step 2 until termination condition is met. |
| Step 4. Output the list of optimal solutions at Pareto front, . |
