DENPSO: A Distance Evolution Nonlinear PSO Algorithm for Energy-Efficient Path Planning in 3D UASNs

Energy optimization is a critical issue in three-dimensional (3D) underwater acoustic sensor networks (UASNs). Intelligent path planning can be applied to extend the lifetime of autonomous underwater vehicles (AUVs) which has attracted many researchers' attention as a key component of UASNs in recent years. In this paper, we put forward an algorithm of distance evolution nonlinear particle swarm optimization (DENPSO), aiming at finding an energy-efficient stable path for AUVs in 3D UASNs. First, in order to ensure that the particles fully explore the 3D underwater environment during the evolution process, we convert the inertia weighting factor and learning factor from linearity to nonlinearity. Second, to avoid particles falling into local optimum regions, the particles of the poor search regions are randomly perturbed by the distance evolution factor. Third, we apply the penalty function to describe the energy optimization goal under the obstacles and ocean currents. To quantify the role of obstacle avoidance in the penalty function, each path is divided into several micro-element points based on the cubic spline interpolation method. Then, we propose a degree value factor to measure the micro-element points falling within the obstacle coverage regions. Finally, simulations are finished in 3D underwater environment and the real environment based on regional ocean model system (ROMS). The results show that DENPSO can avoid the obstacles along the eddy current direction, where the energy consumption of algorithm DENPSO is, respectively, reduced by 2.1514e+03 J and 1.049e+07 J compared with the algorithm LPSO in the above-mentioned environment.