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：合作导引多枚导弹是具有严格的时间和空间一致性限制的挑战性任务，特别是攻击动态目标时更为如此。本文将合作导引任务描述为分布式多目标合作优化问题。为解决合作导引面临的非静态和连续控制问题，提出了一种新的自然协同演化策略 (NCES)。改进了自然进化策略（NES），并结合精英自适应学习技术，提出了自然协同演化策略 (NCES)。将原始进化策略的梯度进行缩放，以减少多弹交互引起的估计偏差。之后，将高度可扩展的协同演化机制和传统导引策略集成，提出了一种混合协同演化合作导引定律 (HCCGL)。最后，通过三个不同条件下的模拟，证明了该导引定律在解决合作导引任务方面的准确性和优越性。所提出的协同演化方法不仅在合作导引中具有广泛前景，而且在多目标优化，动态优化和分布式控制的其他应用场景中也具有潜力。