  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.
