Improved data clustering using multi-trial vector-based differential evolution with Gaussian crossover

被引：6

作者：

Hadikhani, Parham ^{[1
]}

Lai, Daphne Teck Ching ^{[1
]}

Ong, Wee-Hong ^{[1
]}

Nadimi-Shahraki, Mohammad H. ^{[2
]}

机构：

[1] Univ Brunei Darussalam, Sch Digital Sci, Bandar Seri Begawan, Brunei

[2] Islamic Azad Univ, Fac Comp Engn, Najafabad Branch, Najafabad, Iran

来源：

PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022 | 2022年

关键词：

Differential evolution; Data clustering; Data mining; Optimization;

D O I：

10.1145/3520304.3528885

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, an Improved version of the Multi-Trial Vector-based Differential Evolution (IMTDE) algorithm is proposed and adapted for clustering data. The purpose here is to enhance the balance between the exploration and exploitation mechanisms in MTDE by employing Gaussian crossover and modifying the sub-population distribution between the strategies. Results show that IMTDE is superior to the compared algorithms in most 19 datasets used. The code is available here: https://github.com/parhamhadikhani/IMTDE-Clustering.

引用

页码：487 / 490

页数：4