Cooperative guidance of multiple missiles is a challenging task with rigorous constraints of time and space consensus, especially when attacking dynamic targets. In this paper, the cooperative guidance task is described as a distributed multi-objective cooperative optimization problem. To address the issues of non-stationarity and continuous control faced by cooperative guidance, the natural evolutionary strategy (NES) is improved along with an elitist adaptive learning technique to develop a novel natural co-evolutionary strategy (NCES). The gradients of original evolutionary strategy are rescaled to reduce the estimation bias caused by the interaction between the multiple missiles. Then, a hybrid co-evolutionary cooperative guidance law (HCCGL) is proposed by integrating the highly scalable co-evolutionary mechanism and the traditional guidance strategy. Finally, three simulations under different conditions demonstrate the effectiveness and superiority of this guidance law in solving cooperative guidance tasks with high accuracy. The proposed co-evolutionary approach has great prospects not only in cooperative guidance, but also in other application scenarios of multi-objective optimization, dynamic optimization and distributed control.

翻译：多导弹协同制导是一项具有严格时间和空间一致性约束的挑战性任务，尤其在攻击动态目标时。本文将协同制导任务描述为分布式多目标协同优化问题。为解决协同制导面临的非平稳性和连续控制问题，本文改进了自然进化策略（NES），并结合精英自适应学习技术，提出了一种新型自然协同进化策略（NCES）。通过对原始进化策略的梯度进行重新缩放，以减少多导弹相互作用导致的估计偏差。进而，通过整合高度可扩展的协同进化机制与传统制导策略，提出了一种混合协同进化协同制导律（HCCGL）。最后，三种不同条件下的仿真实验证明了该制导律在解决高精度协同制导任务中的有效性和优越性。所提出的协同进化方法不仅在协同制导中，而且在多目标优化、动态优化和分布式控制等其他应用场景中具有广阔前景。