A Modified Gravitational Search Algorithm for Function Optimization

作     者：He, Shoushuai Zhu, Lei Wang, Lei Yu, Lu Yao, Changhua 

作者机构：Army Engn Univ PLA Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2019年第7卷

页      面：5984-5993页

核心收录：

基　　金：National Natural Science Foundation of China [61702543, 71501186] 333 High-Level Talent Training Project of Jiangsu Province of China [BRA 2016542] 

主　　题：Gravitational search algorithm repulsive force exponential Kbest function optimization 

摘      要：Gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton s laws of gravity and motion. Although GSA provides a good performance in solving optimization problems, it has a disadvantage of premature convergence. In this paper, the concept of repulsive force is introduced and the definition of exponential Kbest is used in a new version of GSA, which is called repulsive GSA with exponential Kbest (EKRGSA). In this algorithm, heavy particles repulse or attract all particles according to distance, and all particles search the solution space under the combined action of repulsive force and gravitational force. In this way, the exploration ability of the algorithm is improved and a proper balance between exploration and exploitation is established. Moreover, the exponential Kbest significantly decreases the computational time. The proposed algorithm is tested on a set of benchmark functions and compared with other algorithms. The experimental results confirm the high efficiency of EKRGSA.