Summary. Swarm Intelligence methods have been shown to produce good results in various problem domains. A well-known method belonging to this kind of algorithms is particle swarm optimization (PSO). In this chapter, we examine how adaptation mechanisms can be used in PSO algorithms to better deal with continuous optimization problems. In case of bound-constrained optimization problems, one has to cope with the situation that particles may leave the feasible search space. To deal with such situations, different bound handling methods were proposed in the literature, and it was observed that the success of PSO algorithms highly depends on the chosen bound handling method. We consider how velocity adaptation mechanisms can be used to cope with bounded search spaces. Using this approach we show that the bound handling method becomes less important for PSO algorithms and that using velocity adaptation leads to better results for a wide range of benchmark functions.