In wireless sensor networks Energy-efficient communication is one of the key requirements for a long lifetime. Clustered organization of the sensor nodes is widely acceptable as an energy-efficient routing technique. Finding optimal cluster heads can be considered an NP-hard problem where classical algorithms fail. Therefore, in this paper, a hybrid algorithm biogeography-based optimization (BBO) called KBBOCVI is proposed which combines K-means with BBO and uses cluster validity index (CVI) for measuring the quality of the solutions. The suggested KBBOCVI performance is compared with other cutting-edge techniques such as stable election protocol (SEP), evolutionary routing protocol (ERP), intelligent hierarchical routing (IHCR), and KBBO in terms of residual energy, overall lifespan and network stability. The simulation results validated that the KBBOCVI outperforms others.