Building Large-Scale Drone Defenses from Small-Team Strategies

2026-02-16 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

This study introduces a novel four-stage framework for developing large-scale drone defenses against adversarial swarms, addressing the combinatorial complexity that limits conventional multi-agent optimization methods. The approach combines genetic algorithms (GA) and dynamic programming (DP) to scale strategies from small defender teams (1-8 Blue agents vs. 1-5 Red agents) to significantly larger scenarios (up to 45 Blue agents vs. 30 Red agents). Stage 1 uses GA to evolve effective heuristic combinations for small teams. Stage 2 employs DP to efficiently allocate these small-team strategies to larger forces, transforming a super-exponential search into a polynomial-time problem. Stage 3 samples and combines these optimized sub-chromosomes, while Stage 4 iteratively refines their performance based on large-scale simulation outcomes. The framework, implemented in JAX, demonstrates substantial improvements in defender win rates compared to GA-only baselines, revealing cooperative behaviors that direct optimization struggles to discover.

Key takeaway

For AI Scientists and Research Scientists developing multi-agent systems, this framework offers a robust pathway to overcome scalability challenges in adversarial environments. You should consider adopting a staged GA-DP approach to factorize complex problems, enabling efficient discovery and refinement of high-performing strategies for large-scale drone defense. This method allows you to build effective defenses for up to 45 defenders against 30 attackers, significantly exceeding prior learning-based methods' capabilities.

Key insights

A staged GA-DP framework scales drone defense by evolving small-team heuristics and dynamically allocating them to large swarms.

Principles

• Decompose complex search spaces into tractable sub-problems.
• Iterative refinement improves heuristic generalizability across scales.
• Synergistic chromosome combinations are critical, not individual genes.

Method

The method involves GA for small-team heuristic evolution, DP for polynomial-time allocation of these heuristics to larger teams, sampling of combined sub-chromosomes, and iterative refinement based on large-scale simulation feedback.

In practice

• Use LLMs to generate diverse heuristic candidates.
• Implement simulations in JAX for high-performance computing.
• Evaluate heuristics across multiple scales to ensure robustness.

Topics

• Drone Swarms
• Multi-Agent Systems
• Genetic Algorithms
• Dynamic Programming
• Scalable Defense Strategies

Code references

• jax-ml/jax

Best for: AI Scientist, Research Scientist, AI Researcher, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.