Brain storm optimization is a swarm intelligence algorithm inspired by the brainstorming process in human beings. Many researchers have paid much more attention to it, and many attempts have been made to improve it’s performance. The search ability of brain storm optimization is maintained by the creating process of ideas, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel brain storm optimization variant, named RMBSO, in which a slight relaxation selection and multi-population based creating ideas ensemble are employed to improve the performance of brain storm optimization on global optimization problem with diverse landscapes. Firstly, the basic framework of original brain storm optimization is imbedded into multi-population based ensemble of heterogeneous but complementary creating ideas to make the algorithm jump out of stagnation with strong searching ability. Secondly, a new triangular mutation ruler and a simple partition of subpopulations are designed to better balance exploration and exploitation. Thirdly, a slight relaxation selection mechanism instead of greedy choice is first developed to keep the population’s diversity. Finally, extensive experiments on the suit of CEC 2015 benchmark functions and statistical comparisons are executed. Experimental results indicate that the proposed algorithm is significantly better than, or at least comparable to the state-of-the-art brain storm optimization variants and several improved differential evolution algorithms.

头脑风暴优化是一种受人类头脑风暴过程启发的群体智能算法。许多研究人员对此给予了更多的关注，并且已经进行了许多尝试来改善其性能。头脑风暴优化的搜索能力由思想的创造过程维持，但在开发阶段仍然陷入停滞状态。本文提出了一种新颖的头脑风暴优化变体，名为RMBSO，其中使用了轻微的放松选择和基于多个种群的创建思想集合，以提高针对具有多种景观的全局优化问题的头脑风暴优化的性能。首先，原始脑力激荡优化的基本框架被嵌入到基于异类但互补的多种群集成思想中，从而使该算法以强大的搜索能力跳出停滞状态。其次，设计了一个新的三角突变标尺和一个简单的亚群分区，以更好地平衡勘探和开发。第三，首先建立了一个轻微的放松选择机制，而不是贪婪的选择，以保持人口的多样性。最后，针对CEC 2015基准功能和统计比较进行了广泛的实验。实验结果表明，提出的算法明显优于或至少与最新的头脑风暴优化变体和几种改进的差分进化算法相当。