Ensemble Particle Swarm-Genetic Algorithm with Blockchain-Secured Energy Trading for Coordinated Attack Resilience in Vehicle-to-Grid Networks

Overview

This study presents the Ensemble Particle Swarm-Genetic Algorithm with Blockchain-Secured Energy Trading (EPGA-BET) framework designed to address cybersecurity and transaction integrity challenges in Vehicle-to-Grid networks. The framework integrates metaheuristic optimization, distributed ledger technology, and anomaly detection mechanisms to achieve coordinated resilience against cyber-physical attacks while maintaining operational efficiency in EV energy trading systems.

Methods and approach

The EPGA-BET framework operates through three architecturally decoupled yet interacting components. The primary optimization engine combines Particle Swarm Optimization and Genetic Algorithm operators with a Quantum-inspired PSO formulation to enhance exploration capability while maintaining canonical update mechanisms. Energy trading infrastructure employs a permissioned blockchain architecture utilizing Practical Byzantine Fault Tolerance consensus and Merkle-tree verification structures to guarantee transaction integrity and fault tolerance across distributed nodes. An auxiliary reinforcement learning module performs adaptive anomaly detection by monitoring attack indicators and dynamically adjusting optimization parameters in response to detected threats. The three subsystems interact through defined feedback channels while maintaining architectural independence to prevent cascading subsystem failures.

Key Findings

Comparative experimental evaluation demonstrates statistically significant performance improvements across multiple metrics relative to established baseline strategies. Operational cost reduction, resilience index enhancement, detection latency minimization, and transaction success rate improvement were observed under attack scenarios. Ablation analysis indicates that primary performance gains originate from the PSO-GA ensemble mechanism and the adaptive reinforcement learning component. Blockchain integration substantially enhances transactional trust and system credibility while demonstrating negligible impact on optimization convergence characteristics. The modular architecture maintains system stability despite activated attack conditions.

Implications

The framework establishes a methodological approach for integrating ensemble metaheuristic algorithms with Byzantine-fault-tolerant consensus mechanisms to address simultaneous concerns of computational optimization and cryptographic security in networked energy systems. The decoupled yet interacting architecture provides a transferable design pattern for cyber-physical systems requiring both adaptive optimization and transaction integrity guarantees without subsystem interdependencies that could amplify failure modes.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.