Performance of chaotic switching noise injected to Hopfield NN for quadratic assignment problem

Solving combinatorial optimization problems is one of the important applications of the neural network. Many researchers have reported that exploiting chaos achieves good solving ability. However, the reason of the good effect of chaos has not been clarified yet. In this study, we investigate a performance of chaotic switching noise injected to the Hopfield neural network for quadratic assignment problems. By computer simulation we confirm that the chaotic switching noise is effective for solving quadratic assignment problems as well as intermittent chaos near three-periodic window