Improving modified differential evolution for fuzzy clustering
Sarkar, Jnanendra Prasad ; Saha, Indrajit ; Sarkar, Anasua LU
and Maulik, Ujjwal
(2018)
17th International Conference on Hybrid Intelligent Systems, HIS 2017
In Advances in Intelligent Systems and Computing
734.
p.136-146
- Abstract
Differential evolution is a real value encoded evolutionary algorithm for global optimization. It has gained popularity due to its simplicity and efficiency. Use of special kind of mutation and crossover operators differentiates it from other evolutionary algorithms. In recent times, it has been widely used in different fields of science and engineering. Among recently developed various variants of differential evolution, a modified technique called Modified Differential Evolution based Fuzzy Clustering (MoDEFC-V1), was proposed by the authors of this article to improve the speed and accuracy of convergence of differential evolution with a new mutation operation. However, it has a certain limitation of finding global optimum value while... (More)
Differential evolution is a real value encoded evolutionary algorithm for global optimization. It has gained popularity due to its simplicity and efficiency. Use of special kind of mutation and crossover operators differentiates it from other evolutionary algorithms. In recent times, it has been widely used in different fields of science and engineering. Among recently developed various variants of differential evolution, a modified technique called Modified Differential Evolution based Fuzzy Clustering (MoDEFC-V1), was proposed by the authors of this article to improve the speed and accuracy of convergence of differential evolution with a new mutation operation. However, it has a certain limitation of finding global optimum value while searching in solution space. To overcome the limitation of MoDEFC-V1, in this article, we have proposed two different improved versions of MoDEFC called MoDEFC-V2 and MoDEFC-V3 in order to do the underlying optimization such as clustering of patterns better. The effectiveness of the proposed versions is demonstrated for two synthetic and four real-life datasets. Moreover, the superiority of MoDEFC-V2 and MoDEFC-V3 is shown by comparing with state-of-the-art methods qualitatively and quantitatively. Finally, two sample independent one-tailed t-test is performed in order to judge the superiority of the results produced by the proposed versions.
(Less)
- author
- Sarkar, Jnanendra Prasad
; Saha, Indrajit
; Sarkar, Anasua
LU
and Maulik, Ujjwal
- publishing date
- 2018-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- keywords
- Clustering, Differential evolution, Pattern recognition, Statistical significance test
- host publication
- Hybrid Intelligent Systems : 17th International Conference on Hybrid Intelligent Systems, HIS 2017 - 17th International Conference on Hybrid Intelligent Systems, HIS 2017
- series title
- Advances in Intelligent Systems and Computing
- editor
- Abraham, Ajith ; Muhuri, Pranab Kr. ; Muda, Azah Kamilah and Gandhi, Niketa
- volume
- 734
- pages
- 11 pages
- publisher
- Springer
- conference name
- 17th International Conference on Hybrid Intelligent Systems, HIS 2017
- conference location
- Delhi, India
- conference dates
- 2017-12-14 - 2017-12-16
- external identifiers
-
- scopus:85044471913
- ISSN
- 2194-5357
- ISBN
- 9783319763507
- DOI
- 10.1007/978-3-319-76351-4_14
- language
- English
- LU publication?
- no
- id
- 9f265de8-a376-4d71-8a0e-b56c010ddc4c
- date added to LUP
- 2018-10-09 09:41:30
- date last changed
- 2022-03-25 04:39:15
@inproceedings{9f265de8-a376-4d71-8a0e-b56c010ddc4c,
abstract = {{<p>Differential evolution is a real value encoded evolutionary algorithm for global optimization. It has gained popularity due to its simplicity and efficiency. Use of special kind of mutation and crossover operators differentiates it from other evolutionary algorithms. In recent times, it has been widely used in different fields of science and engineering. Among recently developed various variants of differential evolution, a modified technique called Modified Differential Evolution based Fuzzy Clustering (MoDEFC-V1), was proposed by the authors of this article to improve the speed and accuracy of convergence of differential evolution with a new mutation operation. However, it has a certain limitation of finding global optimum value while searching in solution space. To overcome the limitation of MoDEFC-V1, in this article, we have proposed two different improved versions of MoDEFC called MoDEFC-V2 and MoDEFC-V3 in order to do the underlying optimization such as clustering of patterns better. The effectiveness of the proposed versions is demonstrated for two synthetic and four real-life datasets. Moreover, the superiority of MoDEFC-V2 and MoDEFC-V3 is shown by comparing with state-of-the-art methods qualitatively and quantitatively. Finally, two sample independent one-tailed t-test is performed in order to judge the superiority of the results produced by the proposed versions.</p>}},
author = {{Sarkar, Jnanendra Prasad and Saha, Indrajit and Sarkar, Anasua and Maulik, Ujjwal}},
booktitle = {{Hybrid Intelligent Systems : 17th International Conference on Hybrid Intelligent Systems, HIS 2017}},
editor = {{Abraham, Ajith and Muhuri, Pranab Kr. and Muda, Azah Kamilah and Gandhi, Niketa}},
isbn = {{9783319763507}},
issn = {{2194-5357}},
keywords = {{Clustering; Differential evolution; Pattern recognition; Statistical significance test}},
language = {{eng}},
month = {{01}},
pages = {{136--146}},
publisher = {{Springer}},
series = {{Advances in Intelligent Systems and Computing}},
title = {{Improving modified differential evolution for fuzzy clustering}},
url = {{http://dx.doi.org/10.1007/978-3-319-76351-4_14}},
doi = {{10.1007/978-3-319-76351-4_14}},
volume = {{734}},
year = {{2018}},
}