A Modified Gravitational Search Algorithm for Function Optimization

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.