Optimization with Partially Evolved Hopfield Neural Networks
基于局部进化的Hopfield神经网络的优化计算方法

A novel optimization method using partially evolved Hopfield neural network is proposed in this paper. The method uses Hopfield neural networks and a genetic algorithm on a local area of Hopfield neural networks to compensate each other for defects. The defect of the Hopfield neural network is captured by locally optimal solutions. The defect of genetic algorithms is the lower convergence speed when it optimizes large scale problems. In the proposed method, the Hopfield neural network and a genetic algorithm are used alternately. Solutions obtained with the converged Hopfield neural network are applied to the genetic algorithm to escape from locally optimal solutions. The genetic algorithm is only carried out on some local areas of Hopfield neural network so as to effectively save the computational consumption. The method is evaluated by investigating two large scale optimization problems: image segmentation and 200 cities TSP problem. Experiments show that the local minima of large scale networks can be greatly improved by the partially evolved Hopfield network and the convergence speed is obviously enhanced.