MULTI OBJECTIVE FUNCTION TO TEST PARTICLE SWARM OPTIMIZATION AND R3 CYCLIC PERFORMANCE

Abstract

Particle Swarm Optimization (PSO)and R3 cyclic are the simple optimizer algorithm. In this paper, these algorithms will be compared to find out which one is better and more robust. The criteria will be based on the iteration numbers, NoFe and number of success in finding the optimal solution. The tested functions are the multi objective function with surface aberration. We use traditional stopping criteria with stopping distance 0.001. From the result, in term of the iteration number and number of function to be evaluated, R3 cyclic is better than PSO. The less number of iteration and NoFE will not burden the computation time and cost. But the result that is obtained by R3 cyclic is not always the global optima, sometimes it trapped in the local optima. So in term of accuracy, PSO is better than R3 cyclic. At the end, the term of goodness, effectiveness and better will depend on the term that wants to be reached, if it is the accuracy, the best choice is PSO but if the goal is the cost of computation, R3 cyclic is a better choice.

Keywords: Particle Swarm Optimization, R3 cyclic, Optimizer