The dual-group interaction quantum-behaved particle swarm optimization(QPSO) algorithm based on random evaluation(DIR-QPSO) is proposed by constructing the master-slave sub-groups with different potential well centers, which avoids the rapid disappearance of swarm diversity and enhances the global searching ability through collaboration between sub-groups. Meanwhile, the involvement of random factor further improves the particles’ ability to escape from local extremums. Experiment results on 6 testing functions show that the DIR-QPSO algorithm outperforms the traditional particle swarm optimization(PSO) algorithm regarding the optimization of unimodal and multimodal functions, with enhancement in both convergence speed and precision.