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An improved genetic Hopfield neural networks based on probability model for solving travelling salesman problem

机译:基于概率模型的改进遗传Hopfield神经网络求解旅行商问题

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摘要

The existing problems of Hopfield neural networks sovling travelling salesman problem are analysed and improved energy function is proposed in this paper. Probablity model is introduced into improved HNNs. Probablity model records the gene information of the best individuals,which can make genetic algorithm search simultaneously in depth and width. An improved genetic hopfield neural networks based on probability model is proposed, which not only reduces the rate of invalid tours, but also avoids random search. Simulation experiments show that it can accelerate the convergent speed and enhance the searching ability.
机译:分析了Hopfield神经网络解决旅行商问题的现有问题,并提出了改进的能量函数。概率模型被引入到改进的HNN中。概率模型记录了最佳个体的基因信息,可以使遗传算法在深度和宽度上同时搜索。提出了一种基于概率模型的改进遗传跳跃域神经网络,它不仅降低了无效巡回率,而且避免了随机搜索。仿真实验表明,该算法可以加快收敛速度​​,增强搜索能力。

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