A memetic gravitation search algorithm for solving clustering problems

Ko Wei Huang, Jui Le Chen, Chu Sing Yang, Chun Wei Tsai

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

6 Citations (Scopus)

Abstract

The clustering problem is among the most important optimization problems. Given that it is an NP-hard problem, it can be efficiently solved using meta-heuristic algorithms such as the gravitation search algorithm (GSA). GSA is a new swarm-based algorithm particularly suitable for solving NP-hard combinatorial optimization problems. This paper solves the clustering problem with a newly proposed memetic GSA (MGSA) algorithm. MGSA is coupled with the pattern reduction operator and the multi-start operator. The proposed MGSA algorithm was verified on six UCI benchmarks and images segmentation. Based on a performance comparison amongst MGSA, the original GSA, and two state-of-the-art meta-heuristic algorithms (Firefly algorithm and the Artificial bee colony algorithm), we observe that the proposed algorithm can significantly reduce computation time without compromising much on the quality of the solution.

Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages751-757
Number of pages7
ISBN (Electronic)9781479974924
DOIs
Publication statusPublished - 2015 Sept 10
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: 2015 May 252015 May 28

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

OtherIEEE Congress on Evolutionary Computation, CEC 2015
Country/TerritoryJapan
CitySendai
Period15-05-2515-05-28

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Mathematics

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