Step 1: Set the DE parameters and CR = 0.8. |
Step 2: Initialize the population of m individuals where each decision |
variable , , , and of is set randomly within the interval [1, ]. All |
values must be integers. Considering that and . |
Step 3: Evaluate the objective value J() for all m individuals, and determining the showing |
the best fitness value, such that . |
Step 4: Generate the trial population : |
for (; ; ++) |
do = floor(rand(); while (); |
do = floor(rand(); while (() or ()); |
jrand = floor(rand()); |
for (; ; ++) // generate a trial vector |
if (rand(0,1)<=CR or = jrand) |
; |
else |
; |
end if |
end for |
end for |
Step 5: Evaluate the fitness values () of all trial individuals. Check all |
individuals. If a candidate parameter set is not physically plausible, i.e. out of the |
range [], then an exaggerated cost function value is returned. This aims to |
eliminate ‘‘unstable” individuals. |
Step 6: Select the next population : |
for (; ; ++) |
if |
|
else |
|
end if |
end for |
Step 7: If the iteration number () is met, then the output is the solution (an actual |
ellipse contained in the image), otherwise go back to Step 3. |