An Innovative Application of Swarm-Based Algorithms for Peer Clustering

In most peer-to-peer (P2P) networks, peers are placed randomly or based on their geographical position, which can lead to a performance bottleneck. This problem can be solved by using peer clustering algorithms. In this paper, the significant results of the paper can be described in the following sentences. We propose two innovative swarm-based metaheuristics for peer clustering, slime mold and slime mold K-means. They are competitively benchmarked, evaluated, and compared to nine well-known conventional and swarm-based algorithms: artificial bee colony (ABC), ABC combined with K-means, ant-based clustering, ant K-means, fuzzy C-means, genetic K-means, hierarchical clustering, K-means, and particle swarm optimization (PSO). The benchmarks cover parameter sensitivity analysis and comparative analysis made by using 5 different metrics: execution time, Davies–Bouldin index (DBI), Dunn index (DI), silhouette coefficient (SC), and averaged dissimilarity coefficient (ADC). Furthermore, a statistical analysis is performed in order to validate the obtained results. Slime mold and slime mold K-means outperform all other swarm-inspired algorithms in terms of execution time and quality of the clustering solution.