System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV), serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

Autonomous stealth navigation through modern Integrated Air Defense Systems requires Unmanned Aerial Vehicles (UAVs) to minimize radar detectability while maintaining efficient flight. This poses a severe continuous-control challenge under partial observability, as the dynamic radar cross-section (RCS) is highly coupled with the aircraft’s real-time attitude. In this paper, we formulate this task as a Partially Observable Markov Decision Process (POMDP) and propose an attention-guided deep reinforcement learning framework. Specifically, we design a cross-attention state encoder to adaptively process variable-length, localized radar warning signals, enabling dynamic threat prioritization. The control policy is optimized using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to generate high-precision continuous lateral maneuvers. Extensive simulations demonstrate that our proposed TD3-Att framework achieves improved convergence stability, higher average returns, and robust zero-shot generalization compared to DDPG and SAC baselines. Notably, the learned policy steers the aircraft along low-RCS heading/aspect angles, providing an effective continuous-control solution for navigating contested electromagnetic environments.