Grey wolf gravitational search algorithm

Abstract

Gravitational search algorithm is a nature inspired optimization algorithm, inspired by newton's law of gravity and law of motion. In this paper, a new variant of Gravitational search algorithm is presented. The exploration and exploitation capability of GSA is balanced by splitting the whole swarm into two groups. The search process is modified so that one group better exploits and one group becomes responsible for better exploration. This proposed algorithm is tested over some benchmark functions. The results show that our approach gives a better balance between exploration and exploitation to get the optimal solution. A comparative study of this algorithm with GSA and some well-known swarm based meta-heuristic search methods like Bio-geography based optimization (BBO), Differential evolution (DE) and Artificial bee colony (ABC) confirm its efficiency and robustness.