Particle swarm optimization (PSO) is a well-known and popular computational intelligence (CI) algorithm. The inertia weight of a PSO plays a crucial role exploration and exploitation abilities. Many strategies for adapting the inertia weight of PSOs have been proposed in the recent years. In this study, the adaption for the inertia weight of PSOs was researched. Three ordinary inertia weight controlled PSOs and five chaotic maps-based adaptive inertia weight PSOs were examined in fairly performed comparisons. A total of twenty-three widespread benchmark functions with 10 dimensions for unimodal functions, multimodal functions with many local optima, and multimodal functions with a few local optima are used to evaluate these adaptive inertia weight PSOs. By the comparison of the average performed values, the better methods based on the evaluation results were screened for different benchmark functions which are helpful for solving different applications.