Simultaneously optimizing two or more competing objectives is known as multi-objective optimization, and it is a prevalent situation in real-world issues where trade-offs between objectives need to be carefully handled. For such problems, conventional single-objective optimization approaches are insufficient; instead Multi-objective techniques that can produce a wide range of solutions the Pareto front, which represents the best trade-offs between objectives are required. One well-known technique in this field is Multi-Objective Particle Swarm Optimization (MOPSO), which was inspired by the swarming social behavior of birds. MOPSO is very good in sifting through and taking advantage of the search space to find Pareto-optimal solutions. Nevertheless, in noisy settings where stochastic changes impact the objective functions, its performance may decrease, resulting in poor evaluations of the quality of the solutions and poor convergence. In this study, an updated Multi-objective Particle Swarm Optimization (MOPSO) algorithm is presented, which uses Mean and Weiner filters to reduce the negative effects of noise in the algorithm. Wiener filters, which are ideal in the sense of mean square error, further improve the noise reduction by taking into account both the signal and noise features. The mean filter decreases random fluctuations by averaging multiple evaluations. These filtering techniques improve the algorithm’s adaptability, enabling it to have a more accurate Pareto front, although there is much noise in the problem.