A Chaotic Annealing Neural Network with Gain Sharpening and Its Application to 0-1 Constrained Optimization Problems

摘要

In this article, we propose sharpening the gain of the chaotic annealing neural network to solve 0-1 con- strained optimization problem. During the chaotic annealing, the gain of the neurons gradually increases and finally arrives at a large value. This strategy can accelerate the convergence of the network tot he binary state and keep the satisfaction of the constrains. The simulations, which take the knapsack problems as examples, demonstrate that the approach is efficient both in approximating the global solution and the number of iterations.