# Pastebin skfrDH0m
Zoq : this is the basic framework i have come up with for the pso which uses nsga3 technique for assigning global _guide:
 1. Set the iteration counter, t =0, maximum iterations = T.
 2. Initialize position and velocity of the particles randomly.
 3. Evaluate particles and assign fitness.
 4. while t < T do
 		a. Calculate p(local_best at t+1).
 		b. Calculate p(global_best at t+1) using systematic guide selection.// (here comes the nsga 3 approach through ref. lines and niche   preservation.)
 		c. Update velocity for each particle.
 		d. Update position for each particle.
 		e. Check If the position of the particle has exceeded the bounds –
 If yes, using a boundary handling technique, update the position and velocity for each particle.// an important step
 Else, Keep the same position and velocity as calculated in 4(b) and 4(c).
 f. Evaluate particles and assign fitness.
 g. t  = t + 1
End while

I read your documentation for other optimizers.  So, we can try to implement this algo as per the interface and try running on some functions to see the results. Let me know what you think about this and how to proceed from here.