搜索资源列表
TSPproblems
- 解决TSP问题的几种算法 模拟退火法 遗传算法 贪心算法等-To solve the problem of several TSP algorithm simulated annealing greedy algorithm such as genetic algorithms
samatlab
- 模拟退火算法MATALAB源程序 此题以中国31省会城市的最短旅行路径为例，给出TSP问题的模拟退火程序-Simulated annealing algorithm MATALAB source of this problem of China' s 31 provincial capital cities of the shortest path of travel as an example, simulated annealing procedures for TSP problem
An-expanding-SOM
- 自组织映射（SOM）已成功处理的欧式旅行的鹅岭推销员问题（TSP）。通过将其邻域保持财产和 凸包属性数值模拟TSP，我们引入了一个新的SOM如神经网络，称为前panding的SOM（ESOM）的。在每一个学习的迭代，ESOM提请接近的兴奋神经元 输入的城市，并在此期间，推压它们向凸包ofcities合作。 ESOM可能收购邻里保护财产和凸包的属性 的TSP，因此它可以产生接近最优的解决方案。从理论上分析了其可行性 和经验。一个的系列ofexperiments进行合成和基准的T
Knowledge-base
- In the oPass system, we invite a TSP in the registration phase to accomplish as the same security as physical account setup.
oPass
- In the oPass system, we invite a TSP in the registration phase to accomplish as the same security as physical account setup.
References
- In the oPass system, we invite a TSP in the registration phase to accomplish as the same security as physical account setup.
Synopsis-Format(1)
- In the oPass system, we invite a TSP in the registration phase to accomplish as the same security as physical account setup.
UML-opass-
- In the oPass system, we invite a TSP in the registration phase to accomplish as the same security as physical account setup.
36063823afsa2
- 摘 要:在分析人工鱼群算法存在不足的基础上,对人工鱼群算法加以改进,提出了一种改进型人工鱼群算 法。该算法提高了全局搜索能力和收敛速度,并用于求解具有变量边界约束的非线性复杂函数最优化问题。 仿真结果表明,改进后的人工鱼群算法具有精度高、搜索速度快等特点,是一种求解复杂函数全局最优化的智 能算法-Combinatorial optimization problems through the application of artificial fish-swarm algorithm to imi
ABC
- 蜂群算法解决TSP的问题，以31个城市为例-Problem solving tsp with bee colony algorithm to coordinate data 31 cities in China as a case
weka
- tspData <- read.csv( D:\\weka\\hw\\TSP.csv , header = T, sep = , ) #tspData <- `colnames<-`(tspData,c(1:8)) D <- as.matrix(tspData) tourLength <- function(tour, distMatrix) { tour <- c(tour, tour[1]) route <- embed(tou
Traveling-Salesman-Problem---Nearest-Neighbor
- Nearest Neighbour algorithm for a TSP with 7 cities. The solution changes as the starting point is changed The nearest neighbour (NN) algorithm (a greedy algorithm) lets the salesperson choose the nearest unvisited city as his next move. This algor