Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization

An algorithm based on gradient descent techniques with dynamic tunneling methods for global optimization is proposed. The proposed algorithm consists of gradient descent fur local search and a direct search scheme, based on dynamic tunneling technique, for repelling away from local minimum to find the point of next local descent. This search process applied repeatedly finds the global minimum of an objective function. The convergence properties of the proposed algorithm is validated experimentally on benchmark problems. A comparative computational results confirm the importance of dynamic tunneling in gradient descent techniques.