Brain Storm Optimization Algorithms: A Brief Review

Sicheng Hou, Mingming Zhang, Shi Cheng, Zhile Yang

Abstract: Nowadays, many real-world optimization problems, which are separated and non-differentiable, could not be solved by traditional optimization algorithms efficiently. To deal with those complex problems, Swarm Intelligence (SI) algorithms have been widely applied due to their powerful search ability based on population. Brain Storm Optimization (BSO) algorithm is a young and promising SI algorithm inspired by human beings’ behavior of the brain-storming process. Through the continuous use of convergence and divergence process, individuals in BSO move towards optima over iterations, exploration and exploitation ability of algorithm could also achieve optimal balance. In this paper, the historical development, state-of-the-art, the different variants, and the real-world applications of BSO algorithms are reviewed. Besides, three typical optimization problems and the key points to solve them are also discussed. In the future, BSO algorithms could be used to solve more complex optimization problems, the strength and limitation of various BSO algorithms will be revealed furthermore.