کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6854163 | 1437406 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer
ترجمه فارسی عنوان
الگوریتم های خوشه ای استاتیک و پویا تکاملی مبتنی بر بهینه ساز چند آیه
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کلمات کلیدی
بهینه سازی، متهوریستی، بهینه ساز چند آیه، خوشه بندی هوش روحانی، داده کاوی، فراگیری ماشین، محاسبات تکاملی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Clustering based on nature-inspired algorithms is considered as one of the fast growing areas that aims to benefit from such algorithms to formulate a clustering problem as an optimization problem. In this work, the search capabilities of a recent nature-inspired algorithm called Multi-verse Optimizer (MVO) is utilized to optimize clustering problems in two different approaches. The first one is a static clustering approach that works on a predefined number of clusters. The main objective of this approach is to maximize the distances between different clusters and to minimize the distances between the members in each cluster. In an attempt to overcome one of the major drawbacks of the traditional clustering algorithms, the second proposed approach is a dynamic clustering algorithm, in which the number of clusters is automatically detected without any prior information. The proposed approaches are tested using 12 real and artificial datasets and compared with several traditional and nature-inspired based clustering algorithms. The results show that static and dynamic MVO algorithms outperform the other clustering techniques on the majority of datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 72, June 2018, Pages 54-66
Journal: Engineering Applications of Artificial Intelligence - Volume 72, June 2018, Pages 54-66
نویسندگان
Sarah Shukri, Hossam Faris, Ibrahim Aljarah, Seyedali Mirjalili, Ajith Abraham,