This paper is a review of research in the area of brain storm optimization (BSO) algorithms that examines the “more effective algorithms and search strategies.” This type of technique has application in the real world, for example, to solve problems like finding the optimal locations for setting up devices in electric power systems. The authors acknowledge the initial development of BSO algorithms in 2011, and suggest there have been more than 40 papers associated with this area of research to date. While this number is small, the research is still very new and attracting attention from the swarm intelligence research community.

The BSO algorithm proposes a simple definition to find a solution from several separate clusters in a population. The solutions are clustered and then reexamined since the problem can yield multiple solutions. Each solution may have a better fit for the problem; however, the final choice can be very subjective. The future research of this topic has great real-world problem-solving potential and should attract researchers interested in big data analysis and data mining techniques in general.

In conclusion, this paper is very technical and not suitable for the layperson, but those involved in swarm intelligence and data mining will find it an interesting read.