Maintaining population diversity in brain storm optimization algorithm

Shi Cheng*, Yuhui Shi, Quande Qin, T. O. Ting, Ruibin Bai

*Corresponding author for this work

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

45 Citations (Scopus)


Swarm intelligence suffers the premature convergence, which happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm to measure the change of solutions' distribution is proposed in this paper. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Two kinds of partial re-initialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by these two strategies.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479914883
Publication statusPublished - 16 Sept 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014


Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014


  • Brain storm optimization
  • convergence
  • exploration/exploitation
  • population diversity
  • swarm intelligence

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