Simplex search-based brain storm optimization

Chen, Wei and Cao, Yingying and Cheng, Shi and Sun, Yifei and Liu, Qunfeng and Li, Yun (2018) Simplex search-based brain storm optimization. IEEE Access, 6. pp. 75997-76006. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2018.2883506)

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Abstract

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.

ORCID iDs

Chen, Wei, Cao, Yingying, Cheng, Shi, Sun, Yifei, Liu, Qunfeng and Li, Yun ORCID logoORCID: https://orcid.org/0000-0002-6575-1839;
CORE (COnnecting REpositories)