Multi-Objective Deployment Optimization of Unmanned Underwater Swarms in Realistic Ocean Environments

In marine environmental monitoring, target detection, identification and positioning, and other missions, the detection performance of unmanned underwater swarms directly depends on their deployment strategy. To address complex task scenarios requiring a balance between detection range, positioning accuracy, and connectivity, a multi-objective deployment optimization method is proposed in this paper. First, based on realistic underwater acoustic environment modeling using the Bellhop3D model, a collaborative detection model, positioning accuracy model, and network connectivity model for unmanned underwater platforms are established. These three models are integrated with weighted coefficients to construct the objective function, whose maximum value is sought to achieve optimal detection performance of the unmanned swarm. Second, an improved cuckoo search (ICS) algorithm is designed as the solution tool. The results demonstrate that the proposed deployment model achieves multi-objective balance and exhibits significant advantages over single-objective optimization models. Furthermore, the ICS algorithm obtains better solution quality than the compared intelligent optimization algorithms, providing an effective optimization tool for deployment in complex underwater acoustic environments.