During the last couple of decades, evolutionary and swarm intelligence algorithms have significantly advanced the state of the art for both discrete and numerical optimization. Without niching strategies, they usually converge to a single optimum, even in multimodal search spaces where numerous global or local solutions exist. In the literature, several niching approaches have been proposed for simultaneously computing multiple optima, though most of them require some user-specified parameters that should be calculated a priori, i.e. additional knowledge about the problem domain is required. Recently, it was demonstrated that particle swarm optimization (PSO) using a ring topology for neighborhood definition can give rise to robust and parameterless niching methods. Nevertheless, their performance dramatically worsens when the dimensionality of the solution space hikes, thus increasing the number of local optima. This paper aims at enhancing the performance of these types of PSO-based algorithms by introducing two procedures: (1) a differential operator for improving the search ability and (2) a heuristic clearing operator for controlling the swarm diversity. Such operators are probabilistically activated through a novel self-adaptive learning strategy. Empirical results confirm the superiority of our proposed scheme with respect to six other competitive niching techniques.