An energy efficient hierarchical routing approach for UWSNs using biology inspired intelligent optimization.

Aiming at the issues of uneven energy consumption among nodes and the optimization of cluster head selection in the clustering routing of underwater wireless sensor networks (UWSNs), this paper proposes an improved gray wolf optimization algorithm (CTRGWO-CRP) based on cloning strategy, t-distribution perturbation mutation, and opposition-based learning strategy. Within the traditional gray wolf optimization framework, the algorithm first employs a cloning mechanism to replicate high-quality individuals and introduces a t-distribution perturbation mutation operator to enhance population diversity while achieving a dynamic balance between global exploration and local exploitation. Additionally, it integrates an opposition-based learning strategy to expand the search dimension of the solution space, effectively avoiding local optima and improving convergence accuracy. A dynamic weighted fitness function was designed, which includes parameters such as the average remaining energy of the network and the communication distance from cluster heads to base stations. This function utilizes an adaptive weight adjustment mechanism to achieve multi-objective optimization of energy balance and transmission efficiency. During the cluster head election phase, an elite retention strategy is adopted to prioritize high-energy nodes. In the data transmission phase, a multi-hop relay mechanism based on gradient fields and energy thresholds is constructed, optimizing communication energy consumption through path loss prediction. Simulation results demonstrate that, compared to the LEACH, DMaOWOA, and GSHFA-HCP algorithms, the proposed algorithm significantly extends the network lifetime by at least 23.5%, showcasing its substantial advantages. This verifies the effectiveness of the multi-strategy fusion mechanism in routing optimization.