首页> 外文会议>Applied Sciences and Technology (IBCAST), 2012 9th International Bhurban Conference on >Critical analysis of hopfield's neural network model for TSP and its comparison with heuristic algorithm for shortest path computation
【24h】

Critical analysis of hopfield's neural network model for TSP and its comparison with heuristic algorithm for shortest path computation

机译:TSP的Hopfield神经网络模型的临界分析及其与最短路径计算的启发式算法的比较

获取原文
获取原文并翻译 | 示例

摘要

For shortest path computation, Travelling-Salesman problem is NP-complete and is among the intensively studied optimization problems. Hopfield and Tank's proposed neural network based approach, for solving TSP, is discussed. Since original Hopfield's model suffers from some limitations as the number of cities increase, some modifications are discussed for better performance. With the increase in the number of cities, the best solutions provided by original Hopfield's neural network were considered to be far away from those provided by Lin and Kernighan using Heuristic algorithm. Results of both approaches are compared for different number of cities and are analyzed properly.
机译:对于最短路径计算,Travelling-Salesman问题是NP完全的,并且是深入研究的优化问题之一。讨论了Hopfield和Tank提出的基于神经网络的方法来解决TSP。由于原始的Hopfield模型随着城市数量的增加而受到一些限制,因此讨论了一些改进以获得更好的性能。随着城市数量的增加,原始的Hopfield神经网络提供的最佳解决方案被认为与Lin和Kernighan使用启发式算法提供的解决方案相去甚远。比较了两种方法对不同数量城市的结果,并进行了适当分析。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号