Economic dispatch (ED) is one of the most important problems in terms of energy planning, management, and operation in power systems. This study presents a snow ablation optimizer (SAO) algorithm developed with the fuzzy-based fitness–distance balance (FFDB) method for solving ED problems in small-, medium- and large-scale electric power systems and determining the optimal operating values of fossil fuel thermal generation units. The FFDB-based SAO algorithm (FFDBSAO) controls early convergence problems through balancing exploration–exploitation and improves the solving of high-dimensional optimization problems. In the light of extensive experimental studies conducted on CEC2020, CEC2022, and classical benchmark test functions, the FFDBSAO2 algorithm has shown superior performance against its competitors. Wilcoxon and Friedman’s statistical analysis results confirm the performance and efficiency of the algorithm. Moreover, the proposed algorithm significantly reduces total fuel cost by optimizing fossil fuel thermal generation units. According to the results, the scalability and robustness of the algorithm make it a valuable tool for solving large-scale optimization problems in the planning of electric power systems.

In this study, firstly, the balance between the exploration and exploitation capabilities of the weighted mean of vectors (INFO) algorithm was developed using the fitness–distance balance (FDB) method. Then, the FDB-INFO algorithm was developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success History-based Adaptive Differential Evolution (LSHADE) and a novel Hyper-FDB-INFO algorithm was presented. Finally, the developed Hyper-FDB-INFO algorithm was applied to solve the optimal placement and sizing of FACTS devices for the optimal power flow (OPF) problem incorporating wind energy sources. Moreover, determining the placement and sizing of FACTS devices is an additional problem to minimize the total cost of generation and reducing the power losses of the power system. The experimental results showed that the Hyper-FDB-INFO algorithm is a more effective solver than the SHADE-SF, INFO, FDB-INFO and Hyper-INFO algorithms for wind power and FACTS devices integrating the OPF problem.

The increasing penetration of renewable energy sources and converter‐based distributed generation has significantly intensified power quality disturbance (PQD) challenges in modern distribution systems. Accurate and reliable multi‐class classification of disturbances is therefore essential to ensure grid stability and protect sensitive equipment. In this study, an explainable deep learning framework is proposed for multi‐class PQD classification using electrical structured descriptors (ESD), which provide physically interpretable representations of electrical signals. Three representative architectures—MLP, GRU and BiLSTM—are systematically evaluated under a unified preprocessing and stratified cross‐validation scheme. Model performance is assessed using imbalance‐aware metrics, particularly macro‐averaged measures. Experimental results demonstrate near‐saturated classification performance across all models. The GRU model achieves the best overall accuracy (0.9979) and macro‐F1 score (0.9980), while all models reach an identical macro‐AUC of 0.9998, indicating excellent class separability. These findings suggest that increasing architectural complexity does not necessarily yield significant performance gains when structured and physically meaningful features are employed. To enhance transparency, explainability analyses based on SHAP and LIME are integrated into the framework. The results reveal that RMS voltage, peak voltage and total harmonic distortion (THD) are the most influential features, aligning with established power system knowledge. The proposed framework provides a balanced solution in terms of accuracy, computational efficiency and interpretability, making it suitable for real‐time PQ monitoring in renewable‐integrated smart grid environments.

Classical Optimal Power Flow (OPF) is one of the complex and challenging problem in power system that includes non-convex, nonlinear and large-scale structure. With the use of incorporation of uncertain and intermittent wind energy sources in the OPF problem, the complexity of the problem escalates. In a power system, Flexible Alternating Current Transmission Systems (FACTS) devices can mitigate most of the problems associated with power quality and overload in the network. However, determining the placement and sizing of facts devices is an additional problem to minimize the total cost of production of the power system. As a result, in order to solve OPF problem which includes all of these conditions, an artificial intelligence based optimization algorithm needs to have an unusual exploration ability as well as exploitation–exploration balance. Weighted mean of vectors (INFO) is a new heuristic optimizer, which can help finding a more effective solutions in engineering design optimization problems. In this study, firstly, INFO algorithm was improved by using the Fitness–Distance Balance (FDB) method with its abilities. Then, the algorithm developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success-History based Adaptive Differential Evolution (LSHADE). Finally, the developed algorithm has been applied for solving optimal placement and sizing of facts devices for optimal power flow problem incorporating wind energy source. The obtained results showed that the proposed algorithm is more effective solver for the problems cases while compared to the literature.

Automated gastrointestinal lesion recognition from endoscopic imagery is a clinically demanding task that simultaneously requires sensitivity to fine-grained mucosal texture and awareness of broader spatial organisation within the field of view. Conventional single-backbone networks address one of these requirements well but rarely both, limiting their practical utility in computer-aided colonoscopy workflows. To bridge this gap, we introduce a dual-stream architecture that couples EfficientNet-B3—selected for its parameter-efficient compound-scaling strategy—with the Swin Transformer, whose hierarchical shifted-window self-attention encodes multi-scale contextual dependencies without the quadratic complexity of standard vision transformers. Rather than merging the two streams through static concatenation or simple average pooling, we design a Multi-Head Attention Fusion (MHAF) module that learns, end-to-end, how much each stream should contribute at each spatial position and for each sample. This adaptive gating eliminates redundancy introduced by naive fusion while preserving cross-modal complementarity. The complete pipeline is trained on the eight-class Kvasir endoscopic benchmark under a rigorous experimental protocol. This protocol integrates stratified splitting, aggressive data augmentation, and model selection governed by the Matthews Correlation Coefficient (MCC) a metric particularly well-suited to imbalanced multi-class settings. On the held-out test partition, the framework achieves 95.4% accuracy and an MCC of 0.949. Structured ablation experiments quantify the incremental value of each design decision: the MHAF module alone accounts for a 2–3 percentage-point improvement over the best static-fusion baseline. Cross-dataset evaluation on CVC-ClinicDB and LC25000 suggests that the learned representations retain discriminative capability across related gastrointestinal imaging domains without additional fine-tuning. Grad-weighted Class Activation Maps reveal that the model’s attention consistently falls on pathologically meaningful structures—lesion margins, vascular patterns, and mucosal irregularities—rather than on imaging artefacts, supporting clinical trustworthiness. With an inference latency of 55 ms per frame on a single GPU, the proposed framework is compatible with real-time colonoscopy assistance, offering a concrete step toward reducing inter-observer variability and diagnostic workload in colorectal cancer screening. Cross-dataset evaluation on CVC-ClinicDB and LC25000 confirms that the learned representations transfer across imaging modalities and tissue types without additional fine-tuning.