MGO: A Multi-Granularity Optimization Algorithm Based on Granular-balls

Existing optimization algorithms are designed based on the finest granularity, i.e., a point. This design limits global search capability and reduces efficiency in global exploration. To solve this problem, we propose a multi-granularity optimization algorithm (MGO), a novel evolutionary algorithm that uses granular-balls instead of points for optimization. MGO distributes a large number of granular-balls across the solution space to explore both critical regions, where optimal solutions are likely located, and non-critical regions for a broader search. Small, fine-granularity granular-balls focus on critical regions, while larger, coarse-granularity ones cover less critical areas. Additionally, there is competition and cooperation among different granular-balls to better utilize computational resources. This multi-granularity representation enhances global search efficiency and accelerates convergence. MGO outperforms many popular state-of-the-art algorithms in the CEC2013 single-objective optimization benchmarks and shows clear advantages in the social network influence maximization (IM) problem. With strong approximation capability, fast convergence speed, and simple design, MGO presents a competitive alternative to most existing optimization algorithms.