The rapid growth of renewable integration and active consumer participation has made modern power grids increasingly complex and dynamic, where maintaining balanced and efficient energy distribution remains a central challenge. This paper introduces a symmetry-aware optimized fuzzy deep reinforcement learning-gated recurrent unit (OF-DRL-GRU) model that exploits the natural symmetry and asymmetry in demand–generation behavior to achieve stable and adaptive load balancing. The proposed architecture consists of four core modules: a fuzzy logic layer that formulates symmetrically distributed membership functions for interpretable and balanced state transitions; a DRL agent that governs decision actions through a symmetry-preserving reward mechanism balancing exploration and exploitation; a GRU network that models temporal symmetries while performing controlled symmetry-breaking during dynamic fluctuations to enhance generalization; and an improved multi-objective biogeography-based optimization (IMOBBO) algorithm that optimizes fuzzy parameters and model hyper-parameters through adaptive migration alternating between symmetry preservation and deliberate asymmetry, ensuring efficient convergence and global diversity. The synergy among these modules forms a unified symmetry-aware optimization paradigm, reflecting how symmetric structures sustain stability while purposeful asymmetry enhances robustness and adaptivity. The proposed framework is evaluated using three benchmark datasets (UK-DALE, Pecan Street, and REDD) and compared against several advanced and competitive models. Experimental outcomes show that the proposed OF-DRL-GRU model achieves 99.23% accuracy, 99.69% recall, and 99.83% area under the curve (AUC), alongside faster runtime, lower variance, and improved convergence stability. These results demonstrate that incorporating symmetry–asymmetry principles within AI-driven optimization significantly enhances interpretability, resilience, and energy efficiency, paving the way for intelligent, self-adaptive load management in next-generation smart grids.

The increasing complexity of multi-scale energy systems makes robust anomaly detection essential to ensure system resilience and operational continuity. Recent advances in DL enable effective modeling of high-dimensional, non-linear energy data by capturing latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures.

Forecasting fine particulate matter with a diameter of 2.5 μm (PM_2.5) is critically important due to its adverse effects on human health and environmental sustainability. Although ground-based monitoring stations provide accurate measurements, their limited spatial coverage restricts large-scale PM_2.5 assessment, especially in complex urban regions. Consequently, aerosol optical depth (AOD) derived from satellite imagery, combined with advanced deep learning (DL) techniques, has emerged as an effective alternative by offering wide spatial coverage and rich spatio-temporal information. This paper proposed an optimization-driven fuzzy transformer–deep belief network (ODFT-DBN) for accurate PM_2.5 air pollution prediction. The proposed framework integrates a fuzzy inference module to model uncertainty and nonlinear environmental relationships, a transformer encoder to capture long-range spatio-temporal dependencies, and a DBN to extract hierarchical features and improve prediction robustness. In addition, a novel multi-objective gray wolf optimizer (NMOGWO) is employed to jointly optimize the model hyper-parameters and fuzzy membership functions. The proposed approach is implemented for the city of Tehran, Iran, using meteorological variables, topographical features, ground-based PM_2.5 measurements, and satellite-derived AOD data. The ODFT-DBN model is compared with several benchmark methods, including bidirectional encoder representations from transformers (BERT), transformer, long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), DBN, and extreme gradient boosting (XGBoost). Experimental results demonstrate that the proposed framework achieves superior predictive performance, attaining an R² value of 0.94 and root mean square error (RMSE) of 0.8 μg/m³. Scatter plot analyses indicate a strong agreement between predicted and observed PM_2.5 values, while the proposed model exhibits low variance, stable convergence behavior, and acceptable computational time. Overall, the results confirm the effectiveness, robustness, and practical applicability of the proposed ODFT-DBN framework for spatio-temporal PM_2.5 forecasting.

The rapid proliferation of the Internet of Things (IoT) and cyber-physical systems (CPS) within critical infrastructure sectors has significantly expanded the attack surface for advanced and stealthy cyber threats. Since these systems increasingly rely on real-time data exchange and autonomous control, developing intelligent, scalable, and adaptive anomaly detection mechanisms has become a pressing requirement. This paper proposes a novel hybrid framework, evolutionary-transformer-long short-term memory (Evo-Transformer-LSTM), that integrates the temporal modeling capability of LSTM networks, the global attention mechanism of Transformer encoders, and the optimization power of the improved chimp optimization algorithm (IChOA) for hyper-parameter tuning. In the proposed architecture, the Transformer encoder extracts high-level contextual patterns from traffic sequences, while the LSTM component captures local temporal dependencies. The framework is rigorously evaluated on four benchmark datasets from the Canadian Institute for Cybersecurity (CIC): CIC-IDS-2017, CSE-CIC-IDS-2018, CIC IoT-DIAD (2024), and CICIoV (2024). Comparative experiments are conducted against several state-of-the-art baselines, including transformer, LSTM, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), convolutional neural network (CNN), k-nearest neighbors (KNN), and random forest (RF) classifiers. Results show that the proposed Evo-Transformer-LSTM achieves up to 98.25% accuracy, an F1-score of 97.91%, and an area under the curve (AUC) of 99.36% on CIC-IDS 2017, while maintaining above 96% accuracy and 98% AUC even on the more challenging CICIoV 2024 dataset, consistently surpassing all baseline models. In addition, statistical significance tests confirm the superiority of the proposed approach. In conclusion, Evo-Transformer-LSTM offers a unified, scalable, and robust solution for anomaly detection in modern IoT and CPS infrastructures, with potential for real-world deployment in security-sensitive domains.

The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection (FDI) attacks, posing critical threats to operational integrity, resource management, and public safety. Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated, stealthy threats. To address these challenges, we propose a novel evolutionary transformer-based deep reinforcement learning framework (Evo-Transformer-DRL) designed for robust and adaptive FDI detection in smart water infrastructures. The proposed architecture integrates three powerful paradigms: a transformer encoder for modeling complex temporal dependencies in multivariate time series, a DRL agent for learning optimal decision policies in dynamic environments, and an evolutionary optimizer to fine-tune model hyper-parameters. This synergy enhances detection performance while maintaining adaptability across varying data distributions. Specifically, hyper-parameters of both the transformer and DRL modules are optimized using an improved grey wolf optimizer (IGWO), ensuring a balanced trade-off between detection accuracy and computational efficiency. The model is trained and evaluated on three realistic Industry 4.0 water datasets (secure water treatment (SWAT), water distribution (WADI), and battle of the attack detection algorithms (BATADAL)) which capture diverse attack scenarios in smart treatment and distribution systems. Comparative analysis against state-of-the-art baselines including Transformer, DRL, bidirectional encoder representations from transformers (BERT), convolutional neural network (CNN), long short-term memory (LSTM), and support vector machines (SVM) demonstrates that our proposed Evo-Transformer-DRL framework consistently outperforms others in key metrics such as accuracy, recall, area under the curve (AUC), and execution time. Notably, it achieves a maximum detection accuracy of 99.19%, highlighting its strong generalization capability across different testbeds. These results confirm the suitability of our hybrid framework for real-world Industry 4.0 deployment, where rapid adaptation, scalability, and reliability are paramount for securing critical infrastructure systems.

The rapid evolution of multi-scale energy systems (spanning electricity, hydrogen, and renewable integration) has introduced unprecedented complexity, making robust anomaly detection a critical challenge. The vast heterogeneity and dynamic nature of these systems expose them to faults and cyber–physical risks, where timely detection is vital to ensure resilience, safety, and uninterrupted operation. Nowadays, deep learning (DL) techniques have emerged as powerful tools for modeling large-scale, non-linear, and high-dimensional energy data, enabling the extraction of latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures.