Brown Rat Optimization Algorithm

Swarm intelligence optimization algorithms have made significant progress in recent years in solving both continuous and discrete optimization problems. This paper proposes a novel swarm intelligence algorithm—Brown Rat Optimization (BRO)—inspired by the swarm foraging, social interaction, and risk avoidance behaviors of brown rats. By constructing a mathematical model to describe individual movement trajectories, neighborhood information sharing, and stochastic exploration mechanisms, BRO achieves a balance between global search and local exploitation, avoiding premature convergence. This paper presents the detailed mathematical model, flowchart, and pseudocode of the algorithm, and analyzes its exploration capabilities and convergence. Experimental results show that BRO outperforms traditional particle swarm optimization and ant colony optimization algorithms on standard test functions.