Brain storm optimization with agglomerative hierarchical clustering analysis

Junfeng Chen*, Jingyu Wang, Shi Cheng, Yuhui Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Brain storm optimization (BSO) is a relatively new swarm intelligence algorithm, which simulates the problem-solving process of human brainstorming. In General, BSO employs flat clustering which has a number of drawbacks. In this paper, the agglomerative hierarchical clustering is introduced into BSO and its impact on the performance of the creating operator is then analyzed. The proposed algorithm is applied to numerical optimization problems in comparison with the BSO with k-means Clustering. Experimental results show that the proposed algorithm achieves satisfactory results and guarantees a high coverage rate.

Original languageEnglish
Pages (from-to)115-122
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9713 LNCS
Publication statusPublished - 2016


  • Agglomerative hierarchical clustering
  • Brain storm optimization
  • k-means clustering


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