An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks

摘要

Gravitational search algorithm (GSA) is a recently introduced meta-heuristic that has shown great performance in numerical function optimization and solving real world problems. GSA provides an excellent social interaction between its search agents. This social interaction results in admirable exploration of the search space and gives a unique social component to GSA. However, the social interaction is not able to exploit good solutions in an efficient manner. To overcome this problem, a novel algorithm named as gbest-guided gravitational search algorithm (GG-GSA) has been proposed by utilizing the global best (gbest) solution in the force calculation equation of GSA. The employment of gbest solution in any optimization algorithm is a tough task and can lead to premature convergence. In the proposed algorithm, the gbest solution is used adaptively and is able to achieve a better trade-off between exploration and exploitation. The performance of the proposed algorithm is compared with GSA and its variants on different suites of well-known benchmark test functions. The experimental results show that the GG-GSA performs better than other algorithms for most of the benchmark test functions. Furthermore, to test the ability of the proposed algorithm in solving real world applications, training of feedforward neural network problem is chosen. The results demonstrated the exceptional performance of GG-GSA on real world data-set.