Brain storm optimization algorithm in objective space

Yuhui Shi

doi:10.1109/CEC.2015.7257029

Brain storm optimization algorithm in objective space

Yuhui Shi^*

^*Corresponding author for this work

Department of Electrical and Electronic Engineering

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

96 Citations (Scopus)

Abstract

Brain storm optimization algorithm is a newly proposed swarm intelligence algorithm, which has two main operations, i.e., convergent operation and divergent operation. In the original brain storm optimization algorithm, a clustering algorithm is utilized to cluster individuals into clusters as the convergent operation which is time consuming because of distance calculation during clustering. In this paper, a new convergent operation is proposed to be implemented in the 1-dimensional objective space instead of in the solution space. As a consequence, its computation time will depend on only the population size, not the problem dimension, therefore, a big computation time saving can be obtained which makes it have good scalability. Experimental results demonstrate the effectiveness and efficiency of the proposed brain storm optimization algorithm in objective space.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1227-1234
Number of pages	8
ISBN (Electronic)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257029
Publication status	Published - 10 Sept 2015
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015

Publication series

Name	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

Conference	IEEE Congress on Evolutionary Computation, CEC 2015
Country/Territory	Japan
City	Sendai
Period	25/05/15 → 28/05/15

Keywords

Brain storm optimization algorithm
objective space
swarm intelligence component

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10.1109/CEC.2015.7257029

Cite this

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title = "Brain storm optimization algorithm in objective space",

abstract = "Brain storm optimization algorithm is a newly proposed swarm intelligence algorithm, which has two main operations, i.e., convergent operation and divergent operation. In the original brain storm optimization algorithm, a clustering algorithm is utilized to cluster individuals into clusters as the convergent operation which is time consuming because of distance calculation during clustering. In this paper, a new convergent operation is proposed to be implemented in the 1-dimensional objective space instead of in the solution space. As a consequence, its computation time will depend on only the population size, not the problem dimension, therefore, a big computation time saving can be obtained which makes it have good scalability. Experimental results demonstrate the effectiveness and efficiency of the proposed brain storm optimization algorithm in objective space.",

keywords = "Brain storm optimization algorithm, objective space, swarm intelligence component",

author = "Yuhui Shi",

note = "Funding Information: This paper was partially supported by National Natural Science Foundation of China under Grant Numbers 60975080 and 61273367. Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Congress on Evolutionary Computation, CEC 2015 ; Conference date: 25-05-2015 Through 28-05-2015",

year = "2015",

month = sep,

day = "10",

doi = "10.1109/CEC.2015.7257029",

language = "English",

series = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

}

Shi, Y 2015, Brain storm optimization algorithm in objective space. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257029, 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1227-1234, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 25/05/15. https://doi.org/10.1109/CEC.2015.7257029

Brain storm optimization algorithm in objective space. / Shi, Yuhui.
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1227-1234 7257029 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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PY - 2015/9/10

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N2 - Brain storm optimization algorithm is a newly proposed swarm intelligence algorithm, which has two main operations, i.e., convergent operation and divergent operation. In the original brain storm optimization algorithm, a clustering algorithm is utilized to cluster individuals into clusters as the convergent operation which is time consuming because of distance calculation during clustering. In this paper, a new convergent operation is proposed to be implemented in the 1-dimensional objective space instead of in the solution space. As a consequence, its computation time will depend on only the population size, not the problem dimension, therefore, a big computation time saving can be obtained which makes it have good scalability. Experimental results demonstrate the effectiveness and efficiency of the proposed brain storm optimization algorithm in objective space.

AB - Brain storm optimization algorithm is a newly proposed swarm intelligence algorithm, which has two main operations, i.e., convergent operation and divergent operation. In the original brain storm optimization algorithm, a clustering algorithm is utilized to cluster individuals into clusters as the convergent operation which is time consuming because of distance calculation during clustering. In this paper, a new convergent operation is proposed to be implemented in the 1-dimensional objective space instead of in the solution space. As a consequence, its computation time will depend on only the population size, not the problem dimension, therefore, a big computation time saving can be obtained which makes it have good scalability. Experimental results demonstrate the effectiveness and efficiency of the proposed brain storm optimization algorithm in objective space.

KW - Brain storm optimization algorithm

KW - objective space

KW - swarm intelligence component

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BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Congress on Evolutionary Computation, CEC 2015

Y2 - 25 May 2015 through 28 May 2015

ER -

Brain storm optimization algorithm in objective space

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Keywords

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