Brain Storm Optimization Algorithms with K-medians Clustering Algorithms

Zhu, HY;Shi, YH

[Zhu, Haoyu; Shi, Yuhui] Xian Jiaotong Liverpool Univ, Suzhou, Peoples R China.

2015 SEVENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)

Pages:107-110

DOI:10.1109/ICACI.2015.7184758

Publication Year:2015

Document Type:Conference Paper

Identifier:http://hdl.handle.net/20.500.12791/002540

Abstract

Brain storm optimization (BSO) algorithm is a novel swarm intelligence algorithm inspired by human beings' brainstorming process in problems solving. Generally, BSO algorithm has five main steps, which are initialization, evaluation, clustering, disruption and updating. In these five steps, the clustering step is critical to BSO algorithms. Original BSO algorithms use k-means methods as clustering algorithms, but k-means algorithm is affected by extreme values easily and the speed of algorithm is not high enough. In this paper, a variation of k-means clustering algorithm, called k-medians clustering algorithm, is investigated to replace k-means clustering algorithm. In addition, one modification is applied to both clustering algorithms, which is to replace the calculated cluster center with an individual closest to it. Experimental results show that the effectiveness of BSO does not change obviously, but the higher efficiency can be obtained.

Keywords

Noise Clustering algorithms

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