  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.
