Show simple item record

dc.contributor.authorEl-Abd, M.
dc.date.accessioned2018-11-04T10:06:07Z
dc.date.available2018-11-04T10:06:07Z
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S2210650216301766
dc.identifier.urihttp://hdl.handle.net/11675/5036
dc.description.abstractBrain storm optimization (BSO) is a population-based metaheuristic algorithm that was recently developed to mimic the brainstorming process in humans. It has been successfully applied to many real-world engineering applications involving non-linear continuous optimization. In this work, we propose improving the performance of BSO by introducing a global-best version combined with per-variable updates and fitness-based grouping. In addition, the proposed algorithm incorporates a re-initialization scheme that is triggered by the current state of the population. The introduced Global-best BSO (GBSO) is compared against other BSO variants on a wide range of benchmark functions. Comparisons are based on final solutions and convergence characteristics. In addition, GBSO is compared against global-best versions of other meta-heuristics on recent benchmark libraries. Results prove that the proposed GBSO outperform previous BSO variants on a wide range of classical functions and different problem sizes. Moreover, GBSO outperforms other global-best meta-heuristic algorithms on the well-known CEC05 and CEC14 benchmarks.
dc.publisherSwarm and Evolutionary Computation, Elsevier
dc.titleGlobal-best Brain Storm Optimization Algorithm
dc.typeJournal Article
dc.journal.volumeVol. 37
dc.article.pages27-44


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record