NDLI: Brain storm optimization algorithm: a review

Content Provider	Springer Nature Link
Author	Cheng, Shi Qin, Quande Chen, Junfeng Shi, Yuhui
Copyright Year	2016
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.
Starting Page	445
Ending Page	458
Page Count	14
File Format	PDF
ISSN	02692821
Journal	Artificial Intelligence Review
Volume Number	46
Issue Number	4
e-ISSN	15737462
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-02-26
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Brain storm optimization Developmental swarm intelligence Convergent operation Divergent operation Data analysis Artificial Intelligence (incl. Robotics) Computer Science
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Linguistics and Language

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