A holistic review on gravitational search algorithm and its hybridization with other optimization algorithms

A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.