Metaheuristic algorithms (MAs) are powerful tools for solving complex optimization problems across diverse domains. This comprehensive study analyzes 162 MAs through a unified multi-criteria taxonomy classifying algorithms by control parameters (parameter-free, low-parameter, high-parameter), inspiration sources (biological, physical, human-based), search space scope, and exploration–exploitation balance—alongside bibliometric assessment of publications from 2000 to 2024 (including articles, reviews, books, and conference papers). We evaluate the time complexity of 24 highly cited MAs and their real-world applications in engineering, healthcare, and energy systems. Results demonstrate that different MAs exhibit algorithmic simplicity, often requiring only a small number of control parameters, and are capable of efficient global search. Additionally, several algorithms demonstrate strong adaptability for hybridization with other techniques. However, certain methods are prone to premature convergence, primarily due to unbalanced exploration and exploitation dynamics. The proposed work also critically examines metaphor-inspired algorithms, whose contributions are critically evaluated in light of their conceptual complexity. The rise of such methods has led to redundancy and fragmentation in the field, as many reframe familiar optimization principles using superficial metaphors rather than advancing core algorithmic mechanisms. Bibliometric analysis reveals accelerated growth in MA research (64% journal articles, 31% conference papers, 3% from reviews, 1% from conference reviews, and 1% from book chapters). Similarly, in terms of classification, human-inspired methods constitute the largest category (45%), followed by evaluation-inspired (33%), swarm-inspired (14%), and game-inspired and physics-based algorithms (4%). This work consolidates two decades of advancements, identifies critical performance limitations, and provides practical guidelines for algorithm selection and hybridization to enhance optimization efficiency.

Steganography is the art of covert communication. This paper presents an efficient JPEG steganography scheme based on the block entropy of DCT coefficients and syndrome trellis coding (STC). The proposed cost function explores both the block complexity and distortion effects due to flipping and rounding errors. The STC provides multiple solutions to embed messages to a block of coefficients. The proposed scheme determines the best one with minimal distortion effect. In this way, the total distortions are significantly reduced, which corresponds to less detectability of steganalysis. Compared with similar schemes, experiment results demonstrate the superior performance of the proposed scheme in terms of secure embedding capacity against steganalysis.

An intelligent Internet-of-Things (IoT) hardware system in the field tea plantations was built, comprising collection of tea images by HD zoom cameras in a cluster structure and deployment of the detection model by cluster-head edge computing nodes. Data was sent to customer premise equipment through edge nodes and gateways and then to cloud platforms, which provided a hardware platform for identifying remote tea disease online. Field-placed cameras were used as the main acquisition means to study various diseases on Yashixiang, a typical variety of Chaozhou Dancong tea, in different seasons and weather conditions and shooting angles in a natural year period with complex backgrounds. In turn, we constructed a natural environment high-quality dataset covering major diseases e.g., tea anthracnose, tea leaf blight, tea grey blight, Pseudocercospora theae, etc. and explored the feasibility of deep learning algorithms for automatic identification of Chaozhou Dancong tea diseases. Results showed that the recognition rate of Swim Transformer reached 94% in complex natural environments. This paper demonstrated the effectiveness of the dataset and the feasibility of deep learning algorithms applied to the automatic identification of diseases of Chaozhou Dancong tea, laying a foundation for the practical application of the technology in complex natural environments.

Nowadays, cluster analyses are widely used in mental health research to categorize student stress levels. However, conventional clustering methods experience challenges with large datasets and complex issues, such as converging to local optima and sensitivity to initial random states. To address these limitations, this research work introduces an Improved Grey Wolf Clustering Algorithm (iGWCA). This improved approach aims to adjust the convergence rate and mitigate the risk of being trapped in local optima. The iGWCA algorithm provides a balanced technique for exploration and exploitation phases, alongside a local search mechanism around the optimal solution. To assess its efficiency, the proposed algorithm is verified on two different datasets. The dataset-I comprises 1100 individuals obtained from the Kaggle database, while dataset-II is based on 824 individuals obtained from the Mendeley database. The results demonstrate the competence of iGWCA in classifying student stress levels. The algorithm outperforms other methods in terms of lower intra-cluster distances, obtaining a reduction rate of 1.48% compared to Grey Wolf Optimization (GWO), 8.69% compared to Mayfly Optimization (MOA), 8.45% compared to the Firefly Algorithm (FFO), 2.45% Particle Swarm Optimization (PSO), 3.65%, Hybrid Sine Cosine with Cuckoo search (HSCCS), 8.20%, Hybrid Firefly and Genetic Algorithm (FAGA) and 8.68% Gravitational Search Algorithm (GSA). This demonstrates the effectiveness of the proposed algorithm in minimizing intra-cluster distances, making it a better choice for student stress classification. This research contributes to the advancement of understanding and managing student well-being within academic communities by providing a robust tool for stress level classification.