搜索资源列表
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
maugis
- 模拟退火和对称 *欧几里德旅行商问题。 * *为基础的解决办法的本地搜索启发式 *非过境道路和近邻 -/* * Simulated annealing and the Symetric * Euclidian Traveling Salesman Problem. * * Solution based on local search heuristics for * non-crossing paths and nearest neighbor
tsp_ga
- 查找附近的最优解的遗传算法的TSP的使用-Find the optimal solution near the TSP' s use of genetic algorithms
TSP
- 用贪心算法实现tsp(旅行商问题)求解，实现中国34个城市环游路线-With the greedy algorithm tsp (TSP) solution to achieve China' s 34 cities around the line
TSP
- Approximate solution for the Traveling Salesman’s Problem Using Continuous Hopfield Network, matlab TSP
geneticTSP
- 求解TSP问题的性能，包括：运行时间、进化总代数和最优解质量。-to solve TSP s performance issues, including: run time, the evolution of the overall quality of algebra and the optimal solution.
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