Brain storm optimization with agglomerative hierarchical clustering analysis

Junfeng Chen; Jingyu Wang; Shi Cheng; Yuhui Shi

doi:10.1007/978-3-319-41009-8_12

Brain storm optimization with agglomerative hierarchical clustering analysis

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

^*Corresponding author for this work

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	115-122
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9713 LNCS
DOIs	https://doi.org/10.1007/978-3-319-41009-8_12
Publication status	Published - 2016

Keywords

Agglomerative hierarchical clustering
Brain storm optimization
k-means clustering

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10.1007/978-3-319-41009-8_12

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Chen, J., Wang, J., Cheng, S., & Shi, Y. (2016). Brain storm optimization with agglomerative hierarchical clustering analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9713 LNCS, 115-122. https://doi.org/10.1007/978-3-319-41009-8_12

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AU - Cheng, Shi

AU - Shi, Yuhui

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Brain storm optimization with agglomerative hierarchical clustering analysis

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