An Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data Clustering
Ibrahim Al-Marashdeh, Ghaith M. Jaradat, Masri Ayob, Ahmad Abu-Al-Aish and Mutasem Alsmadi
DOI : 10.3844/jcssp.2018.1611.1626
Journal of Computer Science
Volume 14, Issue 12
This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems. BB-BC is a product of an evolution theory of the universe in physics and astronomy. Two main phases of BB-BC are big bang and big crunch. The big bang phase involves a creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into enhancing the BB-BC’s effectiveness in clustering data. Where, the inclusion of an elite pool alongside implicit solution recombination and local search method, contribute to such enhancement. Such strategies resulted in a balanced search of good quality population that is also diverse. The proposed elite pool-based BB-BC was compared with the original BB-BC and other identical metaheuristics. Fourteen different clustering datasets were used to test BB-BC and the elite pool-based BB-BC showed better performance compared to the original BB-BC. BB-BC was impacted more by the incorporated strategies. The experiments outcomes demonstrate the high quality solutions generated by elite pool-based BB-BC. Its performance in fact supersedes that of identical metaheuristics such as swarm intelligence and evolutionary algorithms.
© 2018 Ibrahim Al-Marashdeh, Ghaith M. Jaradat, Masri Ayob, Ahmad Abu-Al-Aish and Mutasem Alsmadi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.