Abstract
Brain Storm Optimization (BSO) algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO.
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