Particle Swarm Optimization (PSO) is a meta-heuristic algorithm which is successfully applied to enormous applications to solve non-linear and complex high-dimensional optimization problems. The performance of PSO is greatly influenced by the tuning parameters. Due to the presence of uncertainty or noise in the optimization problem of different domains and to maintain a balance between exploration and exploitation in the search space, these parameters need to be adjusted. Therefore, it is crucial to identify the optimal values of the tuning parameters. In this paper, a new Belief Rule-Based Adaptive Particle Swarm Optimization (BRBAPSO) is proposed where the tuning parameters are adjusted dynamically by considering uncertainties, which ensure a balance between exploitation and exploration in the search space. Two Variants of BRBAPSO, namely Conjunctive BRBAPSO and Disjunctive BRBAPSO, are introduced and they are compared with Time-Varying Inertia Weight PSO (TVIW-PSO), Time-Varying Acceleration Coefficient PSO (TVAC-PSO), and Fuzzy Adaptive PSO (FAPSO) using the CEC 2013 real-parameter optimization benchmark functions. The results show that both variants of BRBAPSO outperform other algorithms on the benchmark functions.