| Generate the initial population POP and vector of mutation scaling factors; |
| Evaluate the fitness and constraint violations of each individual in POP; |
| For each generation do |
| Sort the individuals in POP based on Deb’s feasibility-based rule (better in front); |
| For each one in POP’s first half |
| Get two new individuals by two DE mutation strategies (rand/1, rand to best/1); |
| Evaluate the fitness value and constraint violations of these two new individuals; |
| Among these two new individuals and corresponding parent individual, the best one is stored into population |
| Tempbest; |
| End for |
| Update the vector of prior probability; |
| For each one in the first half of POP |
| Generate a new individual by Algorithm 1, and store it into population Island; |
| End for |
| For each one in the first half of POP |
| Get one offspring by Algorithm 3 and replace the corresponding individual in population Island with it; |
| End for |
| Go on chaotic search for the first half of POP and the new individuals generated are stored into population tempIsland; |
| Make a contrast between the corresponding ones in Island and tempIsland, and the first half of POP, the best one survives |
| as the corresponding one in POP for next generation; |
| The population Tempbest replace the second half of POP as the parent ones for next generation; |
| Update , by (10), (13) respectively; |
| End for |
