This paper presents a novel computing method of optical efficiency fitting formulas and its applications for parabolic trough solar collectors, by combining the Monte Carlo ray-tracing methodology with the population based particle swarm optimization algorithm. The expressions of the optical efficiency fitting formulas are deduced by means of analyzing the Monte Carlo ray-tracing data samples with a variable reduction technique, while the fitting formula coefficients are obtained using the particle swarm optimization algorithm. The computing time of the annual optical efficiency reduces to less than 10(-7) s, from about a day as previously calculated by a once powerful Monte Carlo ray-tracing model. Thus, it makes the once time-consuming long-time average optical efficiency calculations or corresponding population-based optimizations much more feasible and efficient. A dynamic particle swarm size factor is also proposed to improve the particle swarm optimization performance, which reduces nearly 90% of the particle swarm optimization computing time. Numerical results calculated by the Monte Carlo ray-tracing model and the obtained optical efficiency fitting formulas were compared with the corresponding reference data. Good agreements were obtained, proving that the computing method and model are reliable. The obtained optical efficiency fitting formula of parabolic trough solar collectors is then applied successfully to efficiently study the characteristics of long-time average optical efficiencies under different geometrical configurations, geographical locations, tracking models and corresponding optical or geometrical optimizations. This proposed novel method offers a useful option of high potential for further applications on efficient optical efficiency related analyses and corresponding population-based optimizations for parabolic trough solar collectors.