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<p>This study introduces a novel, mathematically rigorous framework [Quantum Decoherence-Aware Federated Agent-based Meta-Adaptive Reinforcement Evolutionary Hybrid Architecture (<i>QDA-FAMAMREHA</i>)] designed to optimize wind farm control under stochastic atmospheric turbulence and community-imposed environmental constraints. The proposed architecture synergistically integrates quantum coherence modeling, entropy-regularized policy gradient reinforcement learning, hierarchical surrogate-assisted evolutionary search (via <i>NSGA-III</i>), and decentralized federated constraint negotiation mechanisms. By modeling agent-level interactions within a quantum-inspired learning ecosystem, the framework facilitates robust, adaptive control policies that achieve optimal tradeoffs between energy maximization, structural stability, and acoustic mitigation. Simulation results demonstrate the architecture’s efficacy in maintaining coherence stability, policy convergence, and constraint feasibility across diverse operational conditions. The integration of entropy dynamics, Q-value analysis, and policy traceability affirms the model’s capacity to deliver resilient, interpretable, and scalable solutions for next-generation cyber-physical energy infrastructures. This contribution lays the foundation for sustainable, socially aligned, and technically autonomous wind energy systems of the future.</p>

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A quantum decoherence-informed federated multi-agent framework for robust multi-objective wind farm control

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Khamiss Cheikh,
  • El Mostapha Boudi,
  • Rabi Rabi,
  • Hamza Mokhliss

摘要

This study introduces a novel, mathematically rigorous framework [Quantum Decoherence-Aware Federated Agent-based Meta-Adaptive Reinforcement Evolutionary Hybrid Architecture (QDA-FAMAMREHA)] designed to optimize wind farm control under stochastic atmospheric turbulence and community-imposed environmental constraints. The proposed architecture synergistically integrates quantum coherence modeling, entropy-regularized policy gradient reinforcement learning, hierarchical surrogate-assisted evolutionary search (via NSGA-III), and decentralized federated constraint negotiation mechanisms. By modeling agent-level interactions within a quantum-inspired learning ecosystem, the framework facilitates robust, adaptive control policies that achieve optimal tradeoffs between energy maximization, structural stability, and acoustic mitigation. Simulation results demonstrate the architecture’s efficacy in maintaining coherence stability, policy convergence, and constraint feasibility across diverse operational conditions. The integration of entropy dynamics, Q-value analysis, and policy traceability affirms the model’s capacity to deliver resilient, interpretable, and scalable solutions for next-generation cyber-physical energy infrastructures. This contribution lays the foundation for sustainable, socially aligned, and technically autonomous wind energy systems of the future.