The semi-active suspension with Magnetorheological (MR) damper system in a car is crucial for ensuring stability, maintaining continuous road wheel contact, and increasing comfort. Traditional and passive suspension systems are ineffective due to their fixed nature and large amount of force transmitted to the body vehicle. In efforts to enhance performance, different methods have been suggested. One such method involves combining a proportional-integral-derivative (PID) controller with the particle swarm optimization (PSO) algorithm. However, the PSO algorithm has limitations, particularly its ability to thoroughly explore the desired solution space and its tendency to get stuck in local optimum solutions. This has led to the motivation for this study to investigate an alternative metaheuristic approach called the firefly algorithm (FA). The aim of this study is to conduct a comparative analysis between PSO and FA for dynamic analysis in the context of a half-car semi-active suspension system. The study begins with the modelling of the half car semi active suspension including the MR damper system through MATLAB simulation block diagram. Then, the PID controller with PSO and FA algorithms are integrated with the model system. The result from these studies shows that PID-FA gives better performance compared to PID-PSO when compared to passive system in reducing body acceleration with 21.69% and 19.83% respectively. Meanwhile PID-PSO is better in reducing the body displacement compared to PID-FA with percentage reduction of 53.19% and 9.98% respectively. These numbers are based on the simulations that have been done in MATLAB Simulink. This result implies that the FA may improve the riding comfort while also exhibiting the effectiveness of the algorithm when compared to PSO.