Simplex search-based brain storm optimization

Wei Chen, Yingying Cao, Shi Cheng, Yifei Sun, Qunfeng Liu, Yun Li

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
11 Downloads (Pure)


Through modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much more cost to improve the accuracy. To overcome this question in this paper, an excellent direct search-based local solver, the Nelder-Mead Simplex method is adopted in BSO. Through combining BSO's exploration ability and NMS's exploitation ability together, a simplex search-based BSO (Simplex-BSO) is developed via a better balance between global exploration and local exploitation. Simplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions, and alleviate significantly this phenomenon on multimodal functions. Large number of experimental results shows that Simplex-BSO is a promising algorithm for global optimization problems.

Original languageEnglish
Pages (from-to)75997-76006
Number of pages10
JournalIEEE Access
Publication statusPublished - 27 Nov 2018


  • brain storm optimization
  • global exploration
  • local exploitation
  • Nelder-Mead simplex method
  • visualizing confidence intervals

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