Clustering is a fundamental data analysis task that presents challenges. Choosing proper initialization centroid techniques is critical to the success of clustering algorithms, such as k-means. The current work investigates six established methods (random, Forgy, k-means++, PCA, hierarchical clustering, and naive sharding) and three innovative swarm intelligence-based approaches—Spider Monkey Optimization (SMO), Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)—for k-means clustering (SMOKM, WOAKM, and GWOKM). The results on ten well-known datasets strongly favor swarm intelligence-based techniques, with SMOKM consistently outperforming WOAKM and GWOKM. This finding provides critical insights into selecting and evaluating centroid techniques in k-means clustering. The current work is valuable because it provides guidance for those seeking optimal solutions for clustering diverse datasets. Swarm intelligence, especially SMOKM, effectively generates distinct and well-separated clusters, which is valuable in resource-constrained settings. The research also sheds light on the performance of traditional methods such as hierarchical clustering, PCA, and k-means++, which, while promising for specific datasets, consistently underperform swarm intelligence-based alternatives. In conclusion, the current work contributes essential insights into selecting and evaluating initialization centroid techniques for k-means clustering. It highlights the superiority of swarm intelligence, particularly SMOKM, and provides actionable guidance for addressing various clustering challenges.
Energy constraint has become the major challenge for designing wireless sensor networks. Network lifetime is considered as the most substantial metric in these networks. Routing technique is one of the best choices for maintaining network lifetime. This paper demonstrates implementation of new methodology of routing in WSN using firefly swarm intelligence. Energy consumption is the dominant issue in wireless sensor networks routing. For network cutoff avoidance while maximize net lifetime energy exhaustion must be balanced. Balancing energy consumption is the key feature for rising nets lifetime of WSNs. This routing technique involves determination of optimal route from node toward sink to make energy exhaustion balance in network and in the same time maximize network throughput and lifetime. The proposed technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony (ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques in throughput and extend net lifetime.