A Novel Optimization Algorithm Inherited From Gravitational and Spherical Search Dynamics

Abstract

The meta-heuristic is becoming important in the field of modern optimization, and gained more and more attention. In the past thirty years there has been a wide range of meta-heuristic, such as gravitational search algorithm (GSA), has achieved a great success. Spherical search (SS) is the one of newest proposed meta-heuristic algorithms. SS performs search effectively in exploration, but due to the lack of local exploitation ability, it converges slowly and can’t exploit the small region around the current promising solution. This paper proposes a novel optimization algorithm, namely SSGSA, which is inherited from the SS and GSA to combine the effective exploration and exploitation of each algorithm, respectively. To evaluate the effectiveness of SSGSA, we compared it with the original SS, original GSA, particle swarm optimization, and whale optimization algorithm on the IEEE CEC’17 benchmark function suit. Experimental results show that the proposed new method outperforms its competitors in terms of convergence speed and solution accuracy.