Historical knowledge-based MBO for global optimization problems and its application to clustering optimization-Reference-Cited by-同舟云学术

Historical knowledge-based MBO for global optimization problems and its application to clustering optimization

Published:2020-10-26 Issue:5 Volume:25 Page:3485-3501
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Rahbar Mahdi,Yazdani Samaneh

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-020-05381-x.pdf

Reference30 articles.

1. Alcalá-Fdez J, et al (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Log Soft Comput 17

2. Alcalá-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

3. Bacanin N et al (2020) Monarch butterfly optimization based convolutional neural network design. Mathematics 8(6):936

4. Chen M (2020) An enhanced monarch butterfly optimization with self-adaptive crossover operator for unconstrained and constrained optimization problems. Nat Comput

5. Dubes RC, Jain AK (1988) Algorithms for clustering data. Prentice Hall, Englewood Cliffs

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