Particle swarm optimization (PSO) is a population-based global optimization and evolves a group of solutions stochastically, which was motivated by the behavior of a flock of birds or school of fish in nature. PSO is used to solve a wide array of different optimization problems because of its attractive advantages, such as the ease of implementation and its gradient free stochastic algorithm. It has been proved to be an efficient method for many global optimization problems, and not suffering from the difficulties encountered by other evolutionary computation techniques. In this talk, we will review PSO and its variants and then discuss several applications for complicated statistical models. In particular, we will discuss on computational issues of PSO in practice and show how to use it for pharmacokinetics/pharmacodynamics (PK/PD) modeling, including our recent applications to adaptive Phase II clinical trial designs.