In this paper we consider the multi-robot system that collects target objects spread in an unexplored environment. The robots cooperate each other to improve the capability and the efficiency. The robots attract or intimidate each other as behaviors of bacterial swarms or particles with electrical moments. The interactions would increase the working efficiency in some environments but it would decrease the efficiency in some other environments. Therefore, the system needs to adapt to the working environment by adjusting the strengths of the interactions. The strengths of the interactions are expressed as sets of gene codes that mean the weights of each kind of attracting or intimidating vectors. The proposed system adjusts the gene codes using evolutional strategy. The proposed approach has been validated by computer simulation. The results of this paper show that our inter-swarm interacting strategy and optimizing algorithm improves the working efficiency, adaptively to the characteristics of environments.