DOI: 10.14704/nq.2018.16.6.1571

The Global Convergence Analysis of Brain Storm Optimization

Ying Qiao, Yuansheng Huang, Yuelin Gao


Brain storm optimization (BSO) is a novel population-based swarm intelligence algorithm which mimics the human brainstorming process. It transplants the brainstorming process in human being into optimization algorithm design and gains successes in solving many complex optimization problems of the real world. In this paper, the asymptotic convergence properties of BSO are analyzed. Based on the theorem of probability and calculus, it has been proven that BSO, under some assumptions, can asymptotically converge to a global optimal solution set with probability one.


Brain Storm Optimization, Swarm Intelligence Algorithm, Global Convergence Analysis

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