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模拟退火法解决TSP问题
- 使用模拟退火算法计算旅行商问题,内含10城市和20城市的样例代码。
TSPSA.模拟退火算法SA求解TSP旅行商问题
- 模拟退火算法SA求解TSP旅行商问题。可以自己设定初始温度和冷却温度,SA simulated annealing algorithm for solving traveling salesman problem TSP. Can be set for the initial temperature and cooling temperature
TSP.rar
- 模拟退火算法解决旅行商问题,从文件读入城市信息,Simulated annealing algorithm to solve traveling salesman problem, reading from the file information into the city
SA-TSP-Matlab
- 一篇关于模拟退火算法解决TSP问题的MATLAB代码,拿来分享,欢迎讨论-On a simulated annealing algorithm to solve TSP problem of MATLAB code, used to share, please discuss
sa-for-tsp
- 利用模拟退火算法解决50个城市的tsp问题,简单易懂,适合初学者-The use of simulated annealing algorithm to solve 50 problems tsp city, easy-to-read, suitable for beginners
TSPSAwithmatlab
- TSP 问题模拟退火算法的matlab示例-TSP simulated annealing algorithm matlab example
tsp
- 利用模拟退火算法解决旅行商问题,共二十个城市,选择最优路径-The use of simulated annealing algorithm to solve traveling salesman problem
TSP
- 免疫算法和模拟退火算法求解TSP问题的研究 本文提出了一种新的免疫模拟退火算法,并将其应用于求解典型的NP问题—TSP问题 -Immune algorithm and simulated annealing algorithm for solving TSP problems is proposed in this paper a new immune simulated annealing algorithm, and applies it to solve the issue of
TSP
- 模拟退火算法来源于固体退火原理,将固体加温至充分高,再让其徐徐冷却,加温时,固体内部粒子随温升变为无序状,内能增大,而徐徐冷却时粒子渐趋有序,在每个温度都达到平衡态,最后在常温时达到基态,内能减为最小。根据Metropolis准则,粒子在温度T时趋于平衡的概率为e-ΔE/(kT),其中E为温度T时的内能,ΔE为其改变量,k为Boltzmann常数。用固体退火模拟组合优化问题,将内能E模拟为目标函数值f,温度T演化成控制参数t,即得到解组合优化问题的模拟退火算法:由初始解i和控制参数初值t开始,
tsp
- 高级人工智能算法中的TSP问题求解,应用模拟退火算法实现-tsp problem solved in vc++
TSP
- 用模拟退火算法和遗传算法实现TSP旅行商问题,并可以用Matlab对结果进行图形显示分析,非常实用于初学者-Using simulated annealing algorithm and genetic algorithm traveling salesman problem TSP, and the results can be used Matlab graphics analysis, very useful for beginners
模拟退火算法解决tsp问题
- 设计一个可以对TSP问题进行组合优化的连续型Hopfield神经网络模型,利用该魔心可以快速的找到最优的一条路线。(A continuous Hopfield neural network model for combinatorial optimization of TSP problems is designed, which can be used to find the optimal route quickly.)
模拟退火算法
- 基于模拟退火算法的求解TSP问题的matlab程序,对于学习算法的初学者,可作为入门的简单程序(To solve the problem of TSP simulated annealing algorithm based on the matlab program, for beginners learning algorithm, can be used as a simple entry procedures)
模拟退火
- 使用模拟退火算法的特性解决TSP问题,另外可以利用此算法优化其他算法。(Use the simulated annealing algorithm to solve the TSP problem, and you can use this algorithm to optimize other algorithms.)
模拟退火算法
- 使用模拟退火算法求解tsp问题,简单易学(Using simulated annealing algorithm to solve TSP problem, easy to learn)
模拟退火算法tsp
- 用matlab实现模拟退火实现tsp问题。(Using MATLAB to achieve simulated annealing to achieve TSP problem.)
模拟退火tsp问题
- 这篇matlab程序应用模拟退火算法解决tsp问题(This matlab program uses simulated annealing algorithm to solve the TSP problem)
模拟退火算法
- 此问题为传统的TSP问题,从一个城市出发,到达目的地,所用算法为模拟退火算法,算法可以完美运行。(This problem is a traditional TSP problem, starting from a city, reaching the destination, the algorithm is simulated annealing algorithm, the algorithm can run perfectly.)
模拟退火
- 利用模拟退火算法进行仿真实验,解决TSP问题(Using simulated annealing algorithm to solve TSP)
模拟退火算法及其在求解TSP中的应用
- 模拟退火算法(Simulated Annealing,SA)最早的思想是由N. Metropolis [1] 等人于1953年提出。1983 年,S. Kirkpatrick 等成功地将退火思想引入到组合优化领域。它是基于Monte-Carlo迭代求解策略的一种随机寻优算法,其出发点是基于物理中固体物质的退火过程与一般组合优化问题之间的相似性。(The earliest idea of Simulated Annealing (SA) was put forward by N. Metropo