作者：
Chenyang Gao;Xin Yong;Yue-lin Gao*;Teng Li
通讯作者：
Yue-lin Gao
作者机构：
The School of Computer Science and Engineering, Xidian University, Xi’an, China
Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan, China
The School of Computer Science and Engineering, Xidian University, Xi’an, China
Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan, China
The School of Computer Science and Engineering, Xidian University, Xi’an, China
通讯机构：
The School of Computer Science and Engineering, Xidian University, Xi’an, China
Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan, China
语种：
英文
关键词：
Black hole algorithm,Data clustering,Logarithmic spiral path,Self-adaptive parameter
期刊：
COMPLEX & INTELLIGENT SYSTEMS
ISSN：
2199-4536
年：
2024
基金类别：
This work was supported by the Key Project of Ningxia Natural Science Foundation [2022AAC02043], the National Natural Science Foundation of China under Grant [11961001], the Natural Science Foundation of NingXia Hui Autonomous Region [2021AAC03185], the Research Startup Foundation of North Minzu University [2020KYQD23], First-class Discipline Construction Fund project of Ningxia Higher Education [NXYLXK2017B09], and Major scientific Research Project of Northern University for Nationalities [ZDZX201901], Basic discipline research projects supported by Nanjing Securities [NJZQJCXK202201].
摘要：
Data clustering has attracted the interest of scholars in many fields. In recent years, using heuristic algorithms to solve data clustering problems has gradually become a tendency. The black hole algorithm (BHA) is one of the popular heuristic algorithms among researchers because of its simplicity and effectiveness. In this paper, an improved self-adaptive logarithmic spiral path black hole algorithm (SLBHA) is proposed. SLBHA innovatively introduces a logarithmic spiral path and random vector path to BHA. At the same time, a parameter is used to control the randomness, which enhances the local exploitation ability of the algorithm. Besides, SLBHA designs a replacement m...
