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

Adham Salih*, Amiram Moshaiov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number111210
JournalApplied Soft Computing
StatePublished - Feb 2024


FundersFunder number
Ministry of Science and Technology, Israel


    • Adaptive guidance laws
    • Decomposition based evolutionary many-objective optimization
    • Evolutionary computation
    • Many-objective optimization
    • Many-scenarios
    • Neuro-evolution
    • Pareto-optimality
    • Pursuit-evasion
    • Three-dimensional interceptor


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