提出一种基于局部进化的Hopfield神经网络优化计算方法，该方法将遗传算法和Hopfield神经网络结合在一起，克服了Hopfield神经网络易收敛到局部最优值的缺点，以及遗传算法收敛速度慢的缺点。该方法首先由Hopfield神经网络进行状态方程的迭代计算降低网络能量，收敛后的Hopfield神经网络在局部范围内进行遗传算法寻优，以跳出可能的局部最优值陷阱，再由Hopfield神经网络进一步迭代优化。这种局部进化的Hopfield神经网络优化计算方法尤其适合于大规模的优化问题，对图像分割问题和规模较大的200城市旅行商问题的优化计算结果表明，其全局收敛率和收敛速度明显提高。

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.