Abstract: Case-based reasoning (CBR) which stores old problems and solution information as cases can solve new problems of the particle swarm optimization (PSO) with its long-term memory during the learning phase for multiple robots in an unknown environment. The PSO components which offer trainings to the robot in specially-designed simulation environments to deliver basic behaviors enhance their robustness and adaptivity. The CBR components which selects solution from the case base evolved for basic behaviors rank them according to their performance in the new complex enviroment and introduce them to a PSO algorithm's initial population, hence speeding up the learning process. Behavior-based multi-robot formation control task is employed as a platform to assess the effectiveness of this approach with robot simulation software MissionLab. Simulation and experimental results show that the CBR-injected PSO algorithm can quickly obtain optimal control parameters and multi-robot formation performs better in unknown environment.