Abstract
This study introduces a novel strategy that can determine the optimal values of control parameters of a PSO. These optimal control parameters will be very valuable to all the online optimization problems where the convergence time and the failure convergence rate are vital concerns. The newly proposed strategy uses two nested PSO (NESTPSO) searching loops; the inner one contained the original objective function, and the outer one used the inner PSO as a fitness function. The control parameters and the swarm size acted as the optimization variables for the outer loop. These variables were optimized for the lowest premature convergence rate, the lowest number of iterations, and the lowest swarm size. The new proposed strategy can be used for all the swarm optimization techniques as well. The results showed the superiority of the proposed NESTPSO control parameter determination when compared with several state of the art PSO strategies.