In this paper, we introduce Turbulence in the Particle Swarm Optimization (TPSO) and illustrate how this approach could be used for function optimization problems involving high dimensions. The proposed algorithm uses a minimum velocity threshold to control the velocity of particles. TPSO mechanism is similar to a turbulent pump, which supplies some power to the swarm system to explore new search spaces (better solutions). The minimum velocity threshold of the particles is tuned adaptively by using a fuzzy logic controller, which is further called as Fuzzy Adaptive TPSO (FATPSO). We evaluate and compare the performance of SPSO (Standard PSO), TPSO and FATPSO. Empirical results clearly demonstrate that the performance of SPSO degrades remarkably with the increase in the dimensions of the problem, while the influence is very little in the case of TPSO and FATPSO.