Application of Chaos Simulated Annealing in Function Optimization

The performance of Hopfield neural networks was improved through the addition of chaos noise. During the process of optimization, chaos simulated annealing was realized by decaying the amplitude of the chaos noise and the probability of accepting continuously. Adjusting the probability of acceptance could control the speed of chaos simulated annealing, and influence the rate of convergence. The process of optimization was divided into two phases: the coarse search based on chaos and the elaborate search based on gradients. Utilizing the randomicity and ergodicity property of the chaos, the network can get around the global optimal points, and obtain the solutions of optimization. Simulation results proved the validity of the algorithm.