SPECIALSESSIONSBRAIN STORM OPTIMIZATION ALGORITHMS
Shi Cheng, Xiujuan Lei, Simone Ludwig, Yuhui Shi

Swarm intelligence algorithm should have two kinds of ability: capability learning andcapacity developing. The capacity developing focuses on moving the algorithm’s search tothe area(s) where higher search potential may be obtained, while the capability learning
focuses on its actually search from the current solution for single point based optimization
algorithms and from the current population for population-based swarm intelligencealgorithms. The swarm intelligence algorithms with both capability learning and capacity
developing can be called as developmental swarm intelligence algorithms.
The capacity developing is a top-level learning or macro-level learning methodology. Thecapacity developing describes the learning ability of an algorithm to adaptively change its
parameters, structures, and/or its learning potential according to the search states of theproblem to be solved. In other words, the capacity developing is the search strength
possessed by an algorithm. The capability learning is a bottom-level learning or micro-level
learning. The capability learning describes the ability for an algorithm to find better
solution(s) from current solution(s) with the learning capacity it possesses.
The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence, which
is based on the collective behaviour of human being, that is, the brainstorming process. It isnatural to expect that an optimization algorithm based on human collective behaviour could
be a better optimization algorithm than existing swarm intelligence algorithms which arebased on collective behaviour of simple insects, because human beings are social animals
and are the most intelligent animals in the world. The designed optimization algorithm will
naturally have the capability of both convergence and divergence.
The BSO algorithm is a good example of developmental swarm intelligence algorithm. A“good enough” optimum could be obtained through solution divergence and convergence inthe search space. In the BSO algorithm, the solutions are clustered into several categories,
and the new solutions are generated by the mutation of cluster or existing solutions. The
capacity developing, i.e., the adaptation during the search, is another common feature of theBSO algorithms.
The BSO algorithm can be seen as a combination of swarm intelligence and data miningtechniques. Every individual in the brain storm optimization algorithm is not a solution to theproblem to be optimized, but also a data point to reveal the landscapes of the problem. TheSpecial Session Chair
Ke Tang · ketang@ustc.edu.chn

IEEE Conference on Evolutionary Computation
Donostia – San Sebastián, June 8-5
www.cec2017.org · info@cec2017.org

SPECIALSESSIONS

swarm intelligence and data mining techniques can be combined to produce benefits aboveand beyond what either method could achieve alone.
This special session aims at presenting the latest developments of BSO algorithm, as well
as exchanging new ideas and discussing the future directions of developmental swarm
intelligence. Original contributions that provide novel theories, frameworks, and applications
to algorithms are very welcome for this Special Session.

Topics•Analysis and control of BSO parameters•

Parallelized and distributed realizations of BSO algorithms•

BSO for Multimodal optimization•BSO for Multi-objective optimization•BSO for Constrained optimization•BSO for Discrete optimization•

BSO algorithm with data mining techniques•

BSO in uncertain environments•

Theoretical aspects of BSO algorithm•

BSO for Real-world applications

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