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

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.

Formation Control with Connectivity Assurance for Missile Swarms by a Natural Co-Evolutionary Strategy

“…Thus, the Nash equilibrium strategy can be obtained using a co-evolutionary algorithm that is designed for evolving simultaneously to reach the overall optimum fitness. In a previous work [30], we improved the natural evolutionary strategy (NES) and proposed an NCES algorithm that seeks global optimality for the constrained multi-objective optimization problem in multi-agent systems. In brief, the NCES algorithm is a bio-inspired, population-based algorithm capable of optimizing high-dimensional parameters, such as neural network weights, toward the direction of higher fitness.…”

“…Compared with the conventional NES algorithm, the NCES algorithm provides a more accurate estimation of gradients in the presence of multiple interactive agents. More details of the algorithm can be found in [30].…”

“…In [30], we pointed out the importance of the learning rate for the accuracy of the algorithm and proposed an algorithm for learning rate adaptation. However, adjusting the learning rate often consumes a lot of time.…”

“…The foremost motivation of this paper is to develop a metaheuristic evolutionary computational approach to solve the formation control problem for multi-agent systems (MASs) while exploring the usage of neural network (NN) controllers. We use a recently proposed natural co-evolutionary strategy (NCES) algorithm [30] to realize such a vision and apply it to the control of a second-order multi-missile system. The contributions of this paper are manifold.…”