Step 3: mutation. Randomly select three distinct individuals, ,, and , who are all different from the target individual. Generate a perturbed individual by
The scaling factor is constant. denotes the best individuals among the three individuals, which is mean that the one has best fitness function value
Step 4: crossover. The objective function value of each trial vector is compared with that of its corresponding target vector . The vector with the smaller fitness value will be retained in the next generation. Generate a trial individual as follows:
calculate the fitness value of ,
Step 5: Pareto dominance
If ( dominates )
replace by in the current population , and then add to the advanced population
Else
add to the advanced population
End
end
fittest solutions is select in every fast nondominated sorting and save them in ; is the with the lowest fitness value of
