Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides an in-depth review of the applications of GWO and its variants for clustering, multi-hop routing, and hybrid cluster-based routing in WSNs. We categorize and analyze the existing GWO-based approaches across these key network optimization tasks, discussing the different problem formulations, decision variables, objective functions, and performance metrics used. In doing so, we examine standard GWO, multi-objective GWO, and hybrid GWO models that incorporate other computational intelligence techniques. Each method is evaluated based on how effectively it addresses the core constraints of WSNs, including energy consumption, communication overhead, and network lifetime. Finally, this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing. Our goal is to provide researchers and practitioners with a clear, structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.

This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon cancer, three-class lung cancer, and five-class combined lung/colon cancer to conduct feature extraction using four CNNs. These extracted features are then integrated to create a comprehensive feature set. In the next step, the optimization of the feature selection is conducted using a metaheuristic algorithm based on the Electric Eel Foraging Optimization (EEFO). This optimized feature subset is subsequently employed in various ML algorithms to determine the most effective ones through a rigorous evaluation process. The top-performing algorithms are refined using the High-Performance Filter (HPF) and integrated into an ensemble learning framework employing weighted averaging. Our findings indicate that the proposed ensemble learning model significantly surpasses existing methods in classification accuracy across all datasets, achieving accuracies of 99.85% for the two-class, 98.70% for the three-class, and 98.96% for the five-class datasets.