Swarm intelligence algorithms are based on natural

behaviors. Particle swarm optimization (PSO) is a

stochastic search and optimization tool. Changes in the

PSO parameters, namely the inertia weight and the

cognitive and social acceleration constants, affect the

performance of the search process. This paper presents a

novel method to dynamically change the values of these

parameters during the search. Adaptive critic design

(ACD) has been applied for dynamically changing the

values of the PSO parameters.