BSO possess two kinds of functionalities: capability learning and capacity developing. The divergent operation corresponds to the capability learning while the convergent operation corresponds to capacity developing. The capacity developing focuses on moving the algorithm's search to the area(s) where higher potential solutions may exist while the capability learning focuses on its actual search towards new solution(s) from the current solution for single point based optimization algorithms and from the current population of solutions for population-based swarm intelligence algorithms. The capability learning and capacity developing recycle to move individuals towards better and better solutions. The BSO algorithm, therefore, can also be called as a developmental brain storm optimization algorithm.
The capacity developing is a top-level learning or macro-level learning methodology. The capacity 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 the problem to be solved. In other words, the capacity developing is the search potential 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 BSO algorithm can also be seen as a combination of swarm intelligence and data mining techniques. Every individual in the brain storm optimization algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.
Topics of Interest
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.
Potential topics include, but are not limited to:
- Theoretical aspects of BSO algorithms;
- Analysis and control of BSO parameters;
- Parallelized and distributed realizations of BSO algorithms;
- BSO for multiple/many objective optimization;
- BSO for constrained optimization;
- BSO for discrete optimization;
- BSO for large-scale optimization;
- BSO algorithm with data mining techniques;
- BSO in uncertain environments;
- BSO for real-world applications.
Please follow the IEEE CEC2019 instruction for authors and submit your paper via the IEEE CEC 2019 online submission system. Please specify that your paper is for the Special Session on Brain Storm Optimization Algorithms.
Shi Cheng, Shaanxi Normal University, Xi’an, China, cheng#snnu.edu.cn
Junfeng Chen, Hohai University, Changzhou, China, chen-1997#163.com
Yuhui Shi, Southern University of Science and Technology, Shenzhen, China, shiyh#sustc.edu.cn