An Improved artificial sheep algorithm based on a novel hybrid strategy
Tan Ding, Chaoshun Li, Chen Feng, Yanhe Xu · 2021
Artificial sheep algorithm (ASA) is a novel swarm intelligence algorithm. Although ASA performs a good optimization ability, it cannot completely avoid local optimum, premature and imbalance between exploration and exploitation. To solve those problems proposed above, a hybrid improvement strategy is employed on ASA. The hybrid improvement strategy includes two parts, reconstruction strategy and competition strategy which both can not only enhance the diversity of IASA, but also improve the global search ability. Moreover, 13 benchmark functions are run on ASA and the improved ASA named IASA respectively. Test results show that IASA have significantly performed better performance than ASA.