In nature, a phase transition is the transformation of a thermodynamic system from one phase to another. Different phases of a thermodynamic system have distinctive physical properties. Inspired by this natural phenomenon, this paper presents a Particle Swarm Optimization (PSO) based on the Phase Transitions model which consists of solid, liquid and gas phases. Each phase represents a distinctive behavior of the swarm. Transitions of condensation, solidification and deposition can enhance the exploitation capability of the swarm. While the transitions of fusion, vaporization and sublimation from the other direction improve the exploration capability of the swarm. The proposed model directs the swarm to transform among phases dynamically and automatically according to the evolutional states to balance between exploration and exploitation adaptively. Especially, it uses a new modified PSO algorithm called Simple Fast Particle Swarm Optimization (SFPSO) in the solid phase, which modifies the original PSO by adding new parameters simply to make the algorithm convergence more quickly. The proposed algorithm is validated by extensive simulations on the 28 real-parameter optimization benchmark functions from CEC 2013 compared with other three representative variants of PSO.