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Artificial Intelligence Review

, Volume 46, Issue 4, pp 445–458 | Cite as

Brain storm optimization algorithm: a review

  • Shi Cheng
  • Quande QinEmail author
  • Junfeng Chen
  • Yuhui Shi
Article

Abstract

For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Brain storm optimization (BSO) algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. In this paper, the history development, and the state-of-the-art of the BSO algorithm are reviewed. In addition, the convergent operation and divergent operation in the BSO algorithm are also discussed from the data analysis perspective. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Keywords

Brain storm optimization Developmental swarm intelligence Convergent operation Divergent operation Data analysis 

Nomenclature

\( x_{i}\)

The ith dimension of solution \(\mathbf {x}\)

\(p_{\text {generation}}\)

Pre-determined probability, which is used to determine a new individual being generated by one or two “old” individuals

\(p_{\text {oneCluster}}\)

Pre-determined probability, which is used to determine the cluster center or another normal individual will be chosen in one cluster generation case

\(p_{\text {twoCluster}}\)

Pre-determined probability, which is used to determine the cluster center or another normal individual will be chosen in two clusters generation case

r

Random value in the range [0, 1)

\(\xi (t)\)

Step size function

\(f(\mathbf {x})\)

Fitness value: objective function value of \(\mathbf {x}\)

t

Iteration number

T

Maximum number of iteration

S

Population size: the number of solutions in a population

D

Number of decision variables

Notes

Acknowledgments

This work is partially supported by National Natural Science Foundation of China under Grant Number 61403121, 71402103, 61273367, 71240015; the PAPD and CICAEET project; the Foundation for Distinguished Young Talents in Higher Education of Guangdong, China, under Grant 2012WYM_0116; and the MOE Youth Foundation Project of Humanities and Social Sciences at Universities in China under grant 13YJC630123; China Postdoctoral Science Foundation Funded Project (No. 2015M580053); and The Youth Foundation Project of Humanities and Social Sciences in Shenzhen University under grant 14QNFC28; and by Ningbo Science & Technology Bureau (Science and Technology Project Number 2012B10055).

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Division of Computer ScienceUniversity of Nottingham NingboNingboChina
  2. 2.Department of Management ScienceShenzhen UniversityShenzhenChina
  3. 3.College of IOT EngineeringHohai UniversityChangzhouChina
  4. 4.Department of Electrical & Electronic EngineeringXi’an Jiaotong-Liverpool UniversitySuzhouChina
  5. 5.Nanjing University of Information Science and TechnologyNanjingChina

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