A Novel Swarm Exploring Varying Parameter Recurrent Neural Network for Solving Non-Convex Nonlinear Programming
Zhijun Zhang, Xiaohui Ren, Jilong Xie, Yamei Luo
Abstract
Aiming at solving non-convex nonlinear programming efficiently and accurately, a swarm exploring varying parameter recurrent neural network (SE-VPRNN) method is proposed in this article. First, the local optimal solutions are searched accurately by the proposed varying parameter recurrent neural network. After each network converges to the local optimal solutions, information is exchanged through a particle swarm optimization (PSO) framework to update the velocities and positions. The neural network searches for the local optimal solutions again from the updated position until all the neural networks are searched to the same local optimal solution. For improving the global searching ability, wavelet mutation is applied to increase the diversity of particles. Computer simulations show that the proposed method can solve the non-convex nonlinear programming effectively. Compared with three existing algorithms, the proposed method has advantages in accuracy and convergence time.