This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model, each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards the sense it got from both its own history and other particle. The sense factor is calculated by famous Weber-Fechner Law. All these psychology factors will influence the next action of the particle. The resulting algorithm, known as Emotional PSO (EPSO), is shown to perform significantly better than the original PSO algorithm on different benchmark optimization problems. Avoiding premature convergence allows EPSO to continue search for global optima in difficult multimodal optimization problems, reaching better solutions than PSO with a much more fast convergence speed.