A Hybrid Method of Gravitational Search Algorithm and an Adaptive Stochastic Local Search with Application to Function Optimization

In recent years, hybrid algorithms (HAs) have been successfully applied for solving decision and optimization problems. Nevertheless, selecting good algorithms for hybridization has been a crucial issue in HAs. In this paper, a new hybrid algorithm composed of gravitational search algorithm (GSA) and the proposed adaptive stochastic search (ASS) method is introduced. These effective search algorithmsprovide a good trade-off between exploration and exploitation. The performance of the proposed HA is evaluated in the field of numerical function optimization on 23 standard benchmark functions and also on a practical optimization problem, optimal approximation of linear systems. The results are compared with those of some well-known HAs and confirm the efficiency of the proposed method in solving various nonlinear test functions.