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

6 Citations (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 - 2018 Jul 2
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
Country/TerritoryJapan
CityMiyazaki
Period18-10-0718-10-10

All Science Journal Classification (ASJC) codes

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

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