Uncertainty-aware cooperative target search in marine environments: An enhanced Pigeon-Inspired Optimization for multi-AUV systems

This paper presents a novel cooperative search framework for multiple Autonomous Underwater Vehicles (AUVs) operating in uncertain marine environments. Unlike most existing works that focus on static or aerial scenarios, this study targets the dynamic and probabilistic nature of underwater target search tasks, where environmental uncertainty, communication constraints, and ocean currents significantly affect performance. A Cooperative Pigeon-Inspired Optimization (CPIO) algorithm is proposed to improve the global search capabilities and convergence stability of traditional PIO. The CPIO integrates chaotic initialization, bounded velocity correction, and elite retention mechanisms. In addition, an environmental modeling framework is designed based on a Gaussian target probability map, a certainty-aware information graph, and a digital pheromone mechanism, enabling collaborative, efficient, and non-redundant exploration. Extensive simulations under realistic marine constraints demonstrate that the proposed method outperforms several advanced bio-inspired algorithms, including IWOA, SHHO and DWOLF, in terms of search accuracy, coverage rate, and robustness.