Particle Swarm Optimization (PSO) is a population-based algorithm whose performance deteriorates in optimization problems subject to noise. An approach to mitigate the effect of noise is to incorporate resampling methods to evaluate the solutions multiple times and hence estimate better their objective values. The state of the art incorporates a resampling method named Optimal Computing Budget Allocation (OCBA) in order to improve the accuracy of the estimated best solutions. However, the state of the art spends over 95% of the function evaluations on OCBA while the remaining ones are left for PSO to find better solutions. In this paper, we investigate different distributions such that fewer function evaluations are spent on resampling and more on searching. Moreover, we develop a new algorithm in which the function evaluations spent on resampling are further utilized to provide a more robust updating mechanism in PSO via hypothesis testing. Experiments on large-scale function optimization problems with multiplicative Gaussian noise show that our approach has a better overall performance than the state of the art when resampling every two or more iterations. However, the state of the art finds the best solutions when resampling every iteration.