Solution clustering analysis in brain storm optimization algorithm

Shi Cheng, Yuhui Shi, Quande Qin, Shujing Gao

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

43 Citations (Scopus)

Abstract

In swarm intelligence algorithms, premature convergence happens partially due to the solutions getting clustered together, and not diverging again. However, solution clustering is not always harmful for optimization. The solution clustering strategy is utilized in brain storm optimization (BSO) to guide individuals to move toward the better and better areas. The information of clusters indicates the solutions' distribution in the search space, which could be utilized to reveal the landscapes and other proprieties of problems being optimized. In this paper, the solution clustering, and other properties of the brain storm optimization algorithm are analyzed and discussed. Experimental results show that brain storm optimization is a very promising algorithm for solving different kinds of problems.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE Symposium on Swarm Intelligence, SIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Pages111-118
Number of pages8
DOIs
Publication statusPublished - 2013
Event2013 IEEE Symposium on Swarm Intelligence, SIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore
Duration: 16 Apr 201319 Apr 2013

Publication series

NameProceedings of the 2013 IEEE Symposium on Swarm Intelligence, SIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Conference

Conference2013 IEEE Symposium on Swarm Intelligence, SIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Country/TerritorySingapore
CitySingapore
Period16/04/1319/04/13

Keywords

  • Swarm intelligence
  • brain storm optimization
  • convergence
  • exploration/exploitation
  • population diversity
  • solution clustering

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