Enhanced Gravitational Search Algorithm Based on Improved Convergence Strategy

Abstract

Gravitational search algorithm (GSA) is one of the metaheuristic algorithms that has been popularly implemented in solving various optimization problems. The algorithm could perform better in highly nonlinear and complex optimization problems. However, GSA has also been reported to have a weak local search ability and slow searching speed to achieve its convergence. This research proposes two new parameters in order to improve GSA's convergence strategy by improving its exploration and exploitation capabilities. The parameters are the mass ratio and distance ratio parameters. The mass ratio parameter is related to the exploration strategy, while the distance ratio parameter is related to the exploitation strategy of the enhanced GSA (eGSA). These two parameters are expected to create a good balance between the exploration and the exploitation strategies in eGSA. There are seven benchmark functions that have been tested on eGSA. The results have shown that eGSA has been able to produce good performance in the minimization of fitness values and execution times, compared with two other GSA variants. The testing results have shown that the enhancements made to GSA have successfully improved the algorithm's convergence strategy. The improved convergence has also been able to improve the algorithm's solution quality and the processing time. It is expected that eGSA could be applied in many fields and solve various optimization problems efficiently.