An intelligent method of swarm neural networks for equalities-constrained nonconvex optimization

To deal with equalities-constrained nonconvex optimization problem, an intelligence method of swarm neural networks (SNN) is introduced in this paper. The proposed method handles the problem into two parts, which combines local searching ability of one-layer recurrent neural network (RNN) and global searching ability of shuffled frog leaping algorithm (SFLA). First, a RNN model based on general nonconvex optimization is presented. Then the convergence property of RNN is analyzed and proven. Moreover, based on SFLA framework, neural networks are treated as frogs which must be divided into several memeplexes and evolve by their own differential equations to search a local exact solution. Next, through shuffling the best solution of each memeplex, we can obtain the global best point. Finally, numerical examples with simulation results are given to illustrate the effectiveness and good characteristics of the proposed method solving nonconvex optimization problem.