Improved Dingo Optimization Algorithm with Multi-Disturbance Mechanism for 3D Un-derwater Wireless Sensor Network Coverage Optimization

Three-dimensional underwater sensor networks (3D-UWSNs) have significant application value in marine monitoring, resource exploration, and military reconnaissance. However, their three-dimensional deployment is easily affected by issues such as premature convergence, insufficient global search capabilities, and low efficiency in high-dimensional disturbances, leading to low coverage and uneven node distribution. To address this issue, this paper proposes an improved Dingo Optimization Algorithm (IDOA) that introduces three lightweight mechanisms to improve algorithm performance: The Star-Following Learning Strategy (SLS) is based on historical optimal solutions and integrates covariance-adaptive noise to enhance global convergence stability. The Dual Angle Elite Perturbation Strategy (DA-EP) utilizes the Lévy flight characteristics to implement non-equal-scale dual-track perturbations in high-dimensional spaces, effectively improving the ability to escape local optima; the Adaptive Scaling Factor Survival Strategy dynamically adjusts the perturbation amplitude and entropy value to guide the algorithm and achieve a balance between the exploration and exploitation phases. Simulation results show that IDOA outperforms DOA, PSO, SSA, and RIME in most metrics, including coverage, connectivity, and travel distance, in both obstacle-free and obstacle-filled 3D environments. In an obstacle-free environment, it achieves 96.87% coverage and 99% connectivity with 50 nodes deployed; In obstacle-containing scenarios, with 62 nodes deployed, the coverage rate reached 92.07% and the connectivity rate reached 99.84%, verifying the robustness and engineering practicality of the algorithm.