Particle swarm optimization (PSO) is population-based metaheuristic algorithm which mimics animal flocking behavior for food searching and widely applied in various fields. In standard PSO, movement behavior of particles is forced by the current bests, global best and personal best. Despite moving toward the current bests enhances convergence, however, there is a high chance for trapping in local optima. To overcome this local trapping, a new updating equation proposed for particles so-called extraordinary particle swarm optimization (EPSO). The particles in EPSO move toward their own targets selected at each iteration. The targets can be the global best, local bests, or even the worst particle. This approach can make particles jump from local optima. The performance of EPSO has been carried out for unconstrained benchmarks and compared to various optimizers in the literature. The optimization results obtained by the EPSO surpass those of standard PSO and its variants for most of benchmark problems.