Neuro-evolution-based generic missile guidance law for many-scenarios

Designing efficient aerial interceptors is an important task. Using Pareto-optimality, this paper presents a novel approach for generating guidance laws that are generic for many aerial pursuit-evasion scenarios. In particular, the pure-proportional navigation law is combined with a neural network to create adaptive guidance laws, which adapt according to the current state of the system. First, a many-objective optimization problem is formulated in which each objective aims at the best performance in one of the scenarios. Next, using simulations with a many-objective evolutionary algorithm, a population of guidance laws is evolved towards the Pareto-optimal ones. The obtained guidance laws from multiple runs are statistically analyzed and compared with a set of Pareto-optimal pure-proportional navigation laws. The results suggest that the proposed approach provides a significant improvement as compared with the pure-proportional navigation law over the entire set of scenarios.