基于Sobol序列和动态策略的蜜獾算法OA

The Honey Badger Algorithm(HBA)is a recently developed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers,emulating their digging and honey-seeking strategies for exploration and exploitation.However,HBA suffers from limitations such as slow convergence speed,an imbalance between exploration and exploitation,and a tendency to get trapped in local optima.To address these issues,an enhanced version,called the Sobol-based Honey Badger Algorithm(SHBA),is proposed by incorporating Sobol sequences and dynamic strategies.In the initialization phase,Sobol sequences are utilized to improve population quality and diversity,thereby expanding the algorithm's global search capability.Additionally,the original density factor is replaced with an adaptive sine factor to maintain a better balance between exploration and exploitation.To further enhance global search ability and avoid premature convergence,the food acquisition capability and digging phase mechanisms are modified.The performance of SHBA is evaluated on 16 benchmark test functions and compared with several other optimization algorithms,including two improved variants of HBA.Experimental results demonstrate that SHBA significantly improves convergence speed,maintains a strong balance between exploration and exploitation,and effectively avoids local optima.