Stochastic perturbation on variable flipping is the key idea of local search for SAT. Observing that variables are flipped several times in an attempt to escape from a local minimum, this paper presents a duplication learning mechanism in stagnation stages to minimise duplicative variable flipping. The heuristic incorporates the learned knowledge into a variable weighting scheme to effectively prevent the search from selecting duplicative variables. Additionally, probability-based and time window smoothing techniques are adopted to eliminate the effects of redundant information. The integration of the heuristic and gNovelty+ was compared with the original solvers and other state-of-the-art local search solvers. The experimental results showed that the new solver outperformed other solvers on the full set of SAT 2011 competition instances and three sets of real-world verification problems.