The existing intelligent optimization algorithms are designed based on the finest granularity, i.e., a point. This leads to weak global search ability and inefficiency. To address this problem, we proposed a novel multi-granularity optimization algorithm, namely granular-ball optimization algorithm (GBO), by introducing granular-ball computing. GBO uses many granular-balls to cover the solution space. Quite a lot of small and fine-grained granular-balls are used to depict the important parts, and a little number of large and coarse-grained granular-balls are used to depict the inessential parts. Fine multi-granularity data description ability results in a higher global search capability and faster convergence speed. In comparison with the most popular and state-of-the-art algorithms, the experiments on twenty benchmark functions demonstrate its better performance. The faster speed, higher approximation ability of optimal solution, no hyper-parameters, and simpler design of GBO make it an all-around replacement of most of the existing popular intelligent optimization algorithms.

翻译：现有的智能优化算法均基于最细粒度（即点）设计，导致全局搜索能力弱且效率低下。针对该问题，我们通过引入粒球计算，提出了一种新型多粒度优化算法——粒球优化算法（GBO）。GBO使用多个粒球覆盖解空间：大量细粒度的小粒球用于描述重要区域，少量粗粒度的大粒球用于描述非关键区域。这种精细的多粒度数据描述能力带来了更强的全局搜索能力和更快的收敛速度。在20个基准函数上的实验表明，与最流行的先进算法相比，该方法性能更优。GBO具有更快的速度、更强的最优解逼近能力、无超参数设计以及更简洁的结构，使其有望全面替代现有的大多数主流智能优化算法。