Designing Enhanced Automatic Genetic Clustering Algorithm for Unknown Number of Clusters: An Experimental Evaluation

Abstract

This research seeks to develop an improved automatic genetic clustering technique to support the negotiation of heterogeneous and multi-attribute cloud services. It is known that heterogeneous cloud services involve cloud service providers offering varying sizes and costs for cloud service virtual machines, processors and storage. Existing automatic clustering techniques, such as the Automatic Genetic Clustering Algorithm for Unknown Number of Clusters (GCUK) algorithm, can provide the solution for heterogeneous cloud services negotiation, but they can also result in sub-optimal clusters, overlapping and partial solutions, local optima trapping and sub-optimal chromosome size. The automatic clustering technique employs the modified GCUK algorithm to address this issue. It was intended to solve GCUK constraints, local optima, imperfect clustering and suboptimal chromosomal size, as well as to support different cloud services. The findings indicate that the improved GCUK algorithm can provide optimal clustering solutions and avoid local optima, achieve optimal accuracy and resilience and obtain dynamic and optimal chromosomal sizes at runtime.