Improved gravitation search algorithm and its application to function optimization

Abstract

Gravitational Search Algorithm(GSA) easily traps into local optimal solutions and its optimization precision is poor when being applied to function optimization problems.An improved GSA(IGSA) was put forward to solve these problems.It significantly improved the exploration and exploitation abilities of GSA,and had good global and local optimization abilities by introducing opposite learning strategy,elite strategy and boundary mutation strategy.The proposed IGSA had been evaluated on six nonlinear benchmark functions.The experimental results show that,compared with standard GSA,the weighted GSA(WGSA) and Artificial Bee Colony(ABC) algorithms,the IGSA has much better optimization performances in solving various nonlinear functions.