文章基本信息

标题：Improved Black Hole optimization algorithm for data clustering
本地全文：下载
作者：Hasan Deeb ; Archana Sarangi ; Debahuti Mishra 等
期刊名称：Journal of King Saud University @?C Computer and Information Sciences
印刷版ISSN：1319-1578
出版年度：2022
卷号：34
期号：8
页码：5020-5029
语种：English
出版社：Elsevier
摘要：Algorithms inspired by nature became more popular in the last few years. They showed up powerful capability in solving optimization problems. This capability was obtained by their ability to be applied individually or by merging them with other algorithms or techniques. The Black Hole optimization algorithm is a nature-inspired algorithm that belongs to the meta-heuristic category. The Black-Hole algorithm (BH) simulates the black hole phenomenon which is formed from a star with massive size and very high gravitational power. The algorithm starts with a population of a specific size of possible solutions and then gets evaluated by selecting the best one as a black hole. In the suggested modifications, we have introduced a new idea for generating the stars absorbed by the black hole. The star movement towards the black hole has also been modified to increase exploration capabilities. The modified algorithm was used to prove its effectiveness in data clustering without any prior knowledge about the nature of the provided data. Several benchmark datasets and statistical techniques have been used to evaluate the performance of suggested modification. The experiment results promised that the improved algorithm can overcome popular optimization algorithms.