## 2 Related Works
To address these limitations, a novel hierarchical structure named the Hierarchical Chimp Optimization Algorithm (HChOA) for both clustering and multi-hop routing tasks is introduced. The proposed HChOA is evaluated using various metrics through extensive simulations, comparing its performance with different protocols. The results demonstrate that HChOA outperforms others concerning network lifespan and energy consumption. In essence, [PERSON] et al. [35] presented new strategies that improve energy consumption in the network as a whole, even when several important network factors are considered. The network is divided equitably into several clusters by the clustering process, and each cluster elects a CH using PSO, taking into account crucial factors such as SN's RE and distance from the Mobile Sink (MS). Moreover, in the proposed protocol, the movement of the MS is coordinated to distribute energy consumption evenly across all SNs. This is achieved by implementing a circular trajectory with a dynamic radius and constant angular velocity. The MS initiates its movement from the center of the network, traversing along the radius in both forward and reverse directions. This strategic movement pattern ensures that SNs in closer proximity to the sink consume less energy compared to those located farther away, thus enhancing the network's energy efficiency and overall performance. Additionally, the MS broadcasts its initial position, enabling each SN to predict the sink's instantaneous position. Furthermore, the clustering overhead is significantly minimized as the setup phase is infrequently invoked. To evaluate the performance of the proposed protocol regarding network lifetime and overall power consumption across arbitrary rounds, extensive simulations have been conducted, demonstrating conclusively that the innovations embedded in this protocol significantly surpass those of its counterparts. The authors in [36] have introduced a novel optimization algorithm called SWARAM (Osprey Optimization Algorithm-based Energy-Efficient CH Selection) for WSNs in the context of the IoT to optimize CH selection. The SWARAM approach comprises two phases: cluster construction and CH selection, where SNs are first clustered based on Euclidean distance, followed by CH selection using the SWARAM technique. The performance results of SWARAM are compared with existing CH selection and demonstrate that SWARAM enhances packet delivery ratio and network lifetime by 10% each, thereby improving the overall network performance. Ensemble clustering has long demonstrated its prowess in unsupervised learning, but traditional Co-Association (CA) matrix-based methods still fall short in several critical areas. These methods tend to focus solely on strengthening pairwise CA within the same cluster, often neglecting the valuable inter-cluster relationships that provide a more comprehensive understanding of the data. Additionally, they rely heavily on external clustering methods such as spectral clustering to derive results from the CA matrix, and they fail to adaptively assign weights to individual base clustering results, treating all contributions as equally important regardless of their relevance or quality. To address these limitations, the authors in [37] proposed WEC-FCA, a novel Weighted adaptive Ensemble Clustering method powered by a Fuzzy CA (FCA) matrix. The FCA matrix revolutionizes ensemble clustering by capturing both CA and inter-cluster relationships, offering a more nuanced representation of the dataset. By incorporating a rank constraint into the FCA matrix, they designed an innovative optimization framework that directly outputs the optimal ensemble FCA matrix, inherently aligned with the true number of clusters and eliminating the need for external clustering methods. Furthermore, WEC-FCA dynamically assigns weights to each base clustering result using Shannon entropy, ensuring that the most significant contributions are emphasized during the optimization process.