Granular-ball Optimization Algorithm

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.

@article{xia2025_2303.12807,
  title={ GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems },
  author={ Shuyin Xia and Xinyu Lin and Guan Wang and De-Gang Chen and Sen Zhao and Guoyin Wang and Jing Liang },
  journal={arXiv preprint arXiv:2303.12807},
  year={ 2025 }
}