Brain Storm Optimization Model Based on Uncertainty Information

Brain storm optimization is a new swarm intelligence, which mimics the human brainstorming process. In this paper, a modified brain storm optimization is proposed based on uncertainty information. It adopts affinity propagation clustering instead of k-means clustering. Meanwhile, a creating operator...

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Bibliographic Details
Published in2014 Tenth International Conference on Computational Intelligence and Security pp. 99 - 103
Main Authors Junfeng Chen, Yingjuan Xie, Jianjun Ni
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
affinity propagation
brain storm optimization
cloud drops algorithm
Clustering algorithms
Educational institutions
Optimization
Particle swarm optimization
Storms
Uncertainty
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DOI10.1109/CIS.2014.152

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Summary:Brain storm optimization is a new swarm intelligence, which mimics the human brainstorming process. In this paper, a modified brain storm optimization is proposed based on uncertainty information. It adopts affinity propagation clustering instead of k-means clustering. Meanwhile, a creating operator combining the information of multiple clusters is introduced by borrowing the idea of cloud drops algorithm. The proposed brain storm optimization is characterized by mining and utilizing the uncertain information of candidate solutions with no need for the number of clusters. Finally, the modified brain storm optimization is applied to numerical optimization. The simulation results show that the proposed algorithm has better optimization results and higher rate of success than the original version.
DOI:10.1109/CIS.2014.152