Proportional-integral-derivative (PID) control is ubiquitous in industrial automation tasks, and the parameter tuning of the gains is challenging due to nonlinearity and stagnation in local optima. In this paper we present a differential particle scheme based on stagnation-based selection mechanism, and evaluate its effectiveness in the stabilization of a nonlinear inverted pendulum and a magnetic levitation system. Our computational experiments show the feasibility to avoid stagnation, the lower variability of convergence over independent runs, and the feasibility to converge to significantly better fitness values compared to relevant heuristics in the literature. We believe our approach offers the building blocks to build stagnation-free nature inspired optimization algorithms useful for adaptive control and tuning.