An improved gravitational search algorithm for global optimization

Abstract

Gravitational search algorithm (GSA) is inspired by swarm behaviors in nature and physical law based on Newtonian gravity and the laws of motion. There are two key parameters including the number of applied agents ( Kbest ) and gravitational coefficient ( G ) to control the search progress in the algorithm. In the conventional GSA, the acceleration of the agents is mainly determined by Kbest and G. Kbest and G are calculated by a monotonically decreasing function, which is not a good schedule for solving complex problems. In order to solve the problem and accelerate the convergence of algorithm, an adaptive GSA is proposed, in which Kbest and G calculation method for strengthening exploitation capability are implemented to achieve better optimization results. Extensive experimental results based on benchmark functions are provided to show the effectiveness of the proposed method. The obtained results have been compared with the results of the original GSA, CGSA, and CLPSO. The comparison results have revealed that the proposed method has good performances.