Improving Hybrid Gravitational Search Algorithm for Adaptive Adjustment of Parameters
Yongchao Han, Li Ming, Jie Liu · 2017
In this paper, a new Improved Hybrid Gravitational Search Algorithm (IHGSA) is proposed. First, the influence of the learning factor on global exploration and the local exploitation of the algorithm is analyzed, and the parameter adjustment mechanism which can balance the two search capabilities is designed reasonably. Secondly, the PSO is embedded into the Gravitational Search Algorithm (GSA), and an Improved Hybrid Gravitational Search Algorithm (IHGSA) is proposed. Finally, 21 benchmark test functions are programmed and calculated in comparison with the results of the Gravity Search Algorithm (GSA). The numerical results show that the new algorithm balanced the global exploration and local exploitation. The IHGSA are also better than the Gravity Search Algorithm (GSA) in convergence rate and convergence accuracy.