Cooperative guidance of multiple missiles: a hybrid co-evolutionary approach

TL;DR

This paper introduces a novel hybrid co-evolutionary approach for cooperative missile guidance, effectively addressing multi-objective optimization challenges in dynamic environments through an improved natural evolutionary strategy.

Contribution

It develops a new natural co-evolutionary strategy with bias reduction and integrates it into a hybrid guidance law for enhanced multi-missile coordination.

Findings

Demonstrates high accuracy in cooperative guidance simulations

Outperforms traditional guidance strategies in effectiveness

Shows potential for broader applications in multi-objective optimization

Abstract

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.