Computational promise of simultaneous recurrent network with a stochastic search mechanism

This work explores the computational promise of enhancing Simultaneous recurrent neural networks with a stochastic search mechanism as static optimizers. Successful application of simultaneous recurrent neural networks to static optimization problems, where the training had been achieved through one of a number of deterministic gradient descent algorithms including recurrent backpropagation, backpropagation and resilient propagation, was recently reported in the literature. Accordingly at the present time, it became highly desirable to assess if enhancing the neural optimization algorithm with a stochastic search mechanism would be of substantial utility and value, which is the focus of the study reported in this paper. Two techniques are employed to assess the added value of a potential enhancement through a stochastic search mechanism: one method entails comparison of SRN performance with a stochastic search algorithm, the genetic algorithm, and the second method leverages estimation for the quality of optimal solutions through Held-Karp bounds. The traveling salesman problem is employed as the benchmark for the simulation study reported herein. Simulation results suggest that there is likely to be significant improvement possible in the quality of solutions for the traveling salesman problem, and potentially other static optimization problems, if the Simultaneous recurrent neural network is augmented with a stochastic search mechanism.