In this paper, we propose a new approach for self-adaptive particle swarm optimization, using the function's topology to adapt the parameters and modifying them when a planar region is identified in the objective function. Particle swarm optimization is a metaheuristic developed to optimize nonlinear problems. This metaheuristic has four parameters to adapt the search for the different optimization problems. However, finding an optimal set of parameters is not a trivial problem. Some strategies to adapt the parameters have been developed, but they are not robust enough to cover all kinds of problems. Function's topology is one of the most decisive factors in order to choose a right set of parameters; i.e. convex functions need more exploitation because this topology offers a clear direction to the minimum point. In the opposite way, a noise function can be trapped in a local minimum for the same level of exploitation. In order to validate and compare our methods, we use the benchmark functions from CEC 2005 to compare the different particle swarm optimization versions. The results show that the proposed version is significant better than the original particle swarm optimization and the standard particle swarm optimization proposed in 2011.