Brainstorm optimisation (BSO) algorithm is a recently developed swarm intelligence algorithm inspired by the human problem-solving process. BSO has been shown to be an efficient method for creating better ideas to deal with complex problems. The original BSO suffers from low convergence and is easily trapped in local optima due to the improper balance between global exploration and local exploitation. Motivated by the memetic framework, an adaptive BSO with two complementary strategies (AMBSO) is proposed in this study. In AMBSO, a differential-based mutation technique is designed for global exploration improvement and a sub-gradient strategy is integrated for local exploitation enhancement. To dynamically trigger the appropriate strategy, an adaptive selection mechanism based on historical effectiveness is developed. The proposed algorithm is tested on 30 benchmark functions with various properties, such as unimodal, multimodal, shifted and rotated problems, in dimensions of 10, 30 and 50 to verify their scalable performance. Six state-of-the-art optimisation algorithms are included for comparison. Experimental results indicate the effectiveness of AMBSO in terms of solution quality and convergence speed.

中文翻译：

---

多种策略的自适应头脑风暴优化

头脑风暴优化（BSO）算法是受人类问题解决过程启发而开发的一种群体智能算法。BSO已被证明是创建更好的想法来处理复杂问题的有效方法。原始的BSO收敛性低，并且由于全球勘探与本地开发之间的不适当平衡而很容易陷入局部最优状态。在模因框架的激励下，本研究提出了一种具有两种互补策略的自适应BSO（AMBSO）。在AMBSO中，设计了一种基于差异的突变技术来改善全球勘探，并集成了一个次梯度策略来增强本地开发。为了动态触发适当的策略，开发了基于历史有效性的自适应选择机制。所提出的算法在30个具有各种属性的基准函数上进行了测试，例如单峰，多峰，移位和旋转问题，尺寸分别为10、30和50，以验证其可扩展性能。包括六种最先进的优化算法以进行比较。实验结果表明了AMBSO在解决方案质量和收敛速度方面的有效性。