This work presents a comparative study of particle swarm models on their abilities to track extrema in dynamic environments. A standard PSO, two randomized PSOs, and a fine-grained PSO are evaluated in non-trivial multimodal dynamic environments involving small constant step changes, different large step changes, and chaotic step changes of the extrema. DF1 proposed by Morrison and De Jong is used to generate these three types of dynamics (1999). Our results indicate that PSO and its variants are able to perform reasonably well in a 2-dimensional variable space, whereas perform well to a less extent in a 10-dimensional variable space. It is also found that the fine-grained PSO is able to outperform all other PSO variants in the 10-dimensional variable space, likely due to its ability in maintaining better population diversity.