A memetic gravitation search algorithm for solving clustering problems

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

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面751-757
頁數7
ISBN(電子)9781479974924
DOIs
出版狀態Published - 2015 九月 10
事件IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
持續時間: 2015 五月 252015 五月 28

出版系列

名字2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

OtherIEEE Congress on Evolutionary Computation, CEC 2015
國家Japan
城市Sendai
期間15-05-2515-05-28

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Mathematics

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