An Improved Gravitational Coefficient Function for Enhancing Gravitational Search Algorithm’s Performance
Pattrawet Tharawetcharak, Thanathorn Karot, Choosak Pornsing · International Journal of Machine Learning and Computing · 2019
This article proposes a new gravitational coefficient function of the gravitational search algorithm (GSA).Since the function concerns to the performance of GSA, we investigate its characteristic which influences the algorithm on global search performance.The novel function is compared to a former function in literature on four benchmark functions which incorporated of both unimodal landscape functions and multimodal landscape functions.The experimental results show that the proposed gravitational coefficient function outperforms the conventional one.The proposed function also shows that it works well on multimodal landscape functions.By balancing between exploration phase and exploitation phase, the slow convergence rate is compensated by the better solutions.