A Kind of Chaotic Neural Network Optimization Algorithm Based on Annealing Strategy
一种基于退火策略的混沌神经网络优化算法

This paper presents a self organization optimization algorithm,which combines stochastic with deterministic property to introduce chaos mechanism into Hopfield neural network(HNN) to coarsely search the optimum under chaotic dynamics and control the chaotic dynamics by annealing strategy to perform inverse bifurcation and disappear.After that,the gradient property of HNN is used to reach stable point.Simulation results about two typical TSP problems show that such an algorithm,which is robust with initial states,can avoid getting stuck in local minima and has better convergence property as well as time property.Moreover,some conclusions about the effect of parameters on the model are summed up.