Particle Swarm Optimization (PSO) is a population-based algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly when optimization problems are subjected to noise. PSO is strongly influenced by each particle's own previous best one and global best one, which may lead to premature convergence and fall into local optima. This also holds true for various PSO variants dealing with optimization problems in noisy environments. Opposition-based learning (OBL) is well-known for its ability to increase population diversity. In this paper, we propose hybrid PSO algorithms that introduce OBL into a PSO variant for improving the latter's performance. The proposed hybrid algorithm employs probabilistic OBL for particle swarm. In contrast to other integrations of PSO and OBL, we select the top fittest particles from the current swarm and its opposite swarm to improve the entire swarm's fitness. Experiments on 10 benchmark functions subject to different levels of noise show that the proposed algorithm has better performance in most cases.