Recent research on bio-inspired clustering in wireless sensor networks (WSNs) focuses on leveraging nature-inspired optimization algorithms to enhance energy efficiency, scalability, and network lifetime. Techniques based on swarm intelligence such as ant colony optimization, particle swarm optimization, artificial bee colony, and moth-flame algorithms are widely applied for optimal cluster head selection and balanced energy consumption among nodes. Hybrid bio-inspired models combining multiple algorithms have shown improved convergence speed and adaptability under dynamic network conditions. These approaches effectively minimize communication overhead, enhance load distribution, and provide robust and energy-aware clustering solutions suitable for large-scale and heterogeneous WSN deployments.