An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering

Yan Rong Chen, Chun Wei Tsai, Ming Chao Chiang, Chu-Sing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

An improved quantum-inspired evolutionary algorithm (iQEA) is presented in this paper to improve the clustering result of a data clustering problem. Like the other QEA-based algorithms, the iQEA uses Q-bits to denote the state of a quantum particle and Q-gate as an evolutionary operator to guide the search directions. Unlike the fixed rotation degree of QEAs, the rotation degree of iQEA will be changed at different iterations. Experimental results show that the iQEA is able to find a better result than all the other metaheuristic algorithms compared in this paper in terms of quality.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3411-3416
Number of pages6
ISBN (Electronic)9781538666500
DOIs
Publication statusPublished - 2019 Jan 16
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 2018 Oct 72018 Oct 10

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
CountryJapan
CityMiyazaki
Period18-10-0718-10-10

Fingerprint

Evolutionary algorithms
Cluster Analysis
Data clustering

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this

Chen, Y. R., Tsai, C. W., Chiang, M. C., & Yang, C-S. (2019). An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 3411-3416). [8616575] (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00578
Chen, Yan Rong ; Tsai, Chun Wei ; Chiang, Ming Chao ; Yang, Chu-Sing. / An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3411-3416 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).
@inproceedings{4f184dd04a4f41d7bfe92f5bd1294125,
title = "An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering",
abstract = "An improved quantum-inspired evolutionary algorithm (iQEA) is presented in this paper to improve the clustering result of a data clustering problem. Like the other QEA-based algorithms, the iQEA uses Q-bits to denote the state of a quantum particle and Q-gate as an evolutionary operator to guide the search directions. Unlike the fixed rotation degree of QEAs, the rotation degree of iQEA will be changed at different iterations. Experimental results show that the iQEA is able to find a better result than all the other metaheuristic algorithms compared in this paper in terms of quality.",
author = "Chen, {Yan Rong} and Tsai, {Chun Wei} and Chiang, {Ming Chao} and Chu-Sing Yang",
year = "2019",
month = "1",
day = "16",
doi = "10.1109/SMC.2018.00578",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3411--3416",
booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
address = "United States",

}

Chen, YR, Tsai, CW, Chiang, MC & Yang, C-S 2019, An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616575, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3411-3416, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 18-10-07. https://doi.org/10.1109/SMC.2018.00578

An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering. / Chen, Yan Rong; Tsai, Chun Wei; Chiang, Ming Chao; Yang, Chu-Sing.

Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3411-3416 8616575 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering

AU - Chen, Yan Rong

AU - Tsai, Chun Wei

AU - Chiang, Ming Chao

AU - Yang, Chu-Sing

PY - 2019/1/16

Y1 - 2019/1/16

N2 - An improved quantum-inspired evolutionary algorithm (iQEA) is presented in this paper to improve the clustering result of a data clustering problem. Like the other QEA-based algorithms, the iQEA uses Q-bits to denote the state of a quantum particle and Q-gate as an evolutionary operator to guide the search directions. Unlike the fixed rotation degree of QEAs, the rotation degree of iQEA will be changed at different iterations. Experimental results show that the iQEA is able to find a better result than all the other metaheuristic algorithms compared in this paper in terms of quality.

AB - An improved quantum-inspired evolutionary algorithm (iQEA) is presented in this paper to improve the clustering result of a data clustering problem. Like the other QEA-based algorithms, the iQEA uses Q-bits to denote the state of a quantum particle and Q-gate as an evolutionary operator to guide the search directions. Unlike the fixed rotation degree of QEAs, the rotation degree of iQEA will be changed at different iterations. Experimental results show that the iQEA is able to find a better result than all the other metaheuristic algorithms compared in this paper in terms of quality.

UR - http://www.scopus.com/inward/record.url?scp=85062243988&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062243988&partnerID=8YFLogxK

U2 - 10.1109/SMC.2018.00578

DO - 10.1109/SMC.2018.00578

M3 - Conference contribution

AN - SCOPUS:85062243988

T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

SP - 3411

EP - 3416

BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Chen YR, Tsai CW, Chiang MC, Yang C-S. An Improved Quantum-Inspired Evolutionary Algorithm for Data Clustering. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3411-3416. 8616575. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). https://doi.org/10.1109/SMC.2018.00578