The Dandelion Algorithm (DA) is a recently proposed intelligent optimization algorithm inspired by dandelion sowing. For enhancing its exploitation ability and speeding up its convergence, this work proposes a probability-based core dandelion guided dandelion algorithm (GDA). Specifically, the probability of dandelions being selected is calculated firstly. Then the dandelions need to learn from the core dandelion based on previously calculated probability in the process of sowing. Meanwhile, a greedy selection strategy is applied to GDA. Experimental results show that the proposed algorithm not only outperforms DA and its variants, but also outperforms eight state-of-the-art intelligent optimization algorithms on most functions and three real world problems. In addition, the proposed algorithm is applied to optimize kernel extreme learning machine (KELM) for traffic flow prediction, and the results show that the proposed model has a smaller prediction error than its peers.

蒲公英算法（DA）是最近受蒲公英播种启发而提出的智能优化算法。为了提高其开发能力并加快其收敛速度，本文提出了一种基于概率的核心蒲公英制导蒲公英算法（GDA）。具体而言，首先计算选择蒲公英的可能性。然后，在播种过程中，蒲公英需要根据先前计算的概率向核心蒲公英学习。同时，将贪婪选择策略应用于GDA。实验结果表明，该算法不仅性能优于DA及其变体，而且在大多数功能和三个实际问题上也优于八种最新的智能优化算法。此外，