Interval type-2 fuzzy logic systems (IT2FLSs), have recently shown great potential in various applications
with dynamic uncertainties. It is believed that additional degree of uncertainty provided by IT2FL allows
for better representation of the uncertainty and vagueness present in prediction models. However,
determining the parameters of the membership functions of IT2FL is important for providing optimum
performance of the system. Particle Swarm Optimization (PSO) has attracted the interest of researchers
due to their simplicity, effectiveness and efficiency in solving real-world optimization problems. In this
paper, a novel optimal IT2FLS is designed, applied for predicting winning chances in elections. PSO is
used as an optimized algorithm to tune the parameter of the primary membership function of the IT2FL to
improve the performance and increase the accuracy of the IT2F set. Simulation results show the superiority
of the PSO-IT2FL to the similar non-optimal IT2FL system with an increase in the predictio