Secure and optimized drone swarm operations with decentralized Adaptive Differential Evolution

Abstract

Efficient drone swarm management requires real-time adaptive optimization and secure decentralized communication to ensure robust performance in dynamic environments. Traditional optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) suffer from premature convergence and lack the adaptability required for large-scale swarm coordination. Similarly, centralized communication frameworks introduce security vulnerabilities, including single points of failure and susceptibility to cyberattacks. This study presents a novel integration of Adaptive Differential Evolution (ADE) and blockchain technology, leveraging ADE’s dynamic parameter tuning to improve swarm intelligence while utilizing blockchain’s decentralized ledger to secure inter-drone communication. The proposed framework was evaluated through extensive simulations on drone swarms ranging from 20 to 200 drones, demonstrating a 27% improvement in convergence speed and a 35% increase in task efficiency compared to PSO-based methods. Blockchain integration ensured 99.3% data integrity, preventing unauthorized modifications and cyber threats such as man-in-the-middle attacks and data corruption attempts. Energy consumption analysis indicated that ADE reduced power usage by 18% compared to traditional heuristic approaches. Additionally, adversarial testing revealed that denial-of-service (DoS) resilience improved by 42% due to the blockchain’s consensus validation mechanisms. These results highlight the feasibility of secure and adaptive drone swarm management, making it suitable for real-world applications in disaster response, autonomous surveillance, and smart logistics.