One of shortcomings found in the particle swarm optimization algorithm is that it is easy to fall into local optimum

and the opposite learning strategy has a good effect on the improvement of this shortcoming.However

to improve the global search ability by using the opposite learning strategy it is necessary that in the late algorithm other strategies are combined to opposite learning strategy.To overcome this shortcoming

this paper improves the opposite process of the opposite learning strategy according to the refraction principle of light

and proposes the unified model of opposite-based learning(UOBL) and the improved particle swarm optimization algorithm based on the opposite learning model of the principle of refraction(refrPSO).Experiment results and analysis show that the model improves the global search ability of the refrPSO algorithm more effectively compared with other particle swarm algorithm based on opposite learning and the diversity of the population.Because of these improvements

the refrPSO enhances the convergence speed and the accuracy of optimization.