Abstract
This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a "re-fly" mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as "re-fly." To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution.