搜索资源列表
ACO-TSP
- 利用蚁群算法解决旅行商问题，其中运用智能蚂蚁算法-Using Ant Colony Optimization Algorithm to solve Traveling Salesman Problem
ACA
- 蚁群算法用c++语言实现的，可以用于求旅行商问题的解-Ant colony algorithm using c++ language and can be used to seek the solution of traveling salesman problem
AntColonyAlgorithmTsp
- 用蚁群算法求解TSP（旅行商问题）。附上实验报告-ant colony algorithm
antcolonyalgorithm
- 用matlab解释蚁群算法，基于旅行商的问题的介绍-Ant colony algorithm using matlab to explain, based on descr iption of the traveling salesman problem
dataset_602132
- 经典问题TSP（旅行商算法） 蚁群算法训练集-Classic problem TSP (traveling salesman algorithm) ant colony algorithm training set
ACO
- 基于蚁群算法的机器人的路径规划问题蚁群算法，一种与传统的数学规划原理截然不同的，模拟自然生态系统以求解复杂优化问题(如NPC(NP Complete)类问题，典型的有TSP(Traveling Saleman Problem)问题)的仿生优化算法，因其较强分布式计算机制、鲁棒性、易于与其他方法相结合等优点，使得蚁群算法具有较广泛应用领域，为那些最优化技术难以解决的组合优化问题提供了一类新的切实可行的解决方案。从最初的一维的静态优化问题扩展到多维的动态组合优化问题，包括车辆路径规划，工程设计，电力
cSharp
- 蚁群算法解决TSP旅行商问题 C#( 蚁群算法解决TSP旅行商问题 C#( 蚁群算法解决TSP旅行商问题 C#( 蚁群算法解决TSP旅行商问题 C#(-ant colony algorithm C#ant colony algorithm C#ant colony algorithm C#ant colony algorithm C#ant colony algorithm C#ant colony algorithm C#ant colony algorithm C#ant colony al
TSPACO
- 这是一个在VC++ MFC环境下编写的蚁群算法，以51个城市为例实现了旅行商问题的路径规划，每代求出的解以VC++可视化的界面动态显示出来。-This is a prepared environment in VC++ MFC ant colony algorithm to 51 cities traveling salesman problem as an example to achieve a path planning, each generation of the solution o
ACATSP
- 蚁群算法求解旅行商问题的实例，结果直观，值得学习，亲测可用，参数已经修改至最优。-Ant colony algorithm for traveling salesman problem instances, result intuitive, it is worth learning, pro-test is available, the parameters have been modified to the optimum.
MMAS
- 改进的蚁群算法并应用与旅行商问题，相比于没有改进的蚁群算法来说，这个算法能更好的对旅行商寻优问题，寻优结果也挺满意，希望感兴趣的可以借鉴一下。-Improved ant colony algorithm and application of the traveling salesman problem, compared to no improvement ant colony algorithm, this algorithm can better optimization of the tr
chapter22
- 用MATLAB语言编程蚁群算法求解旅行商问题-Ant colony algorithm for traveling salesman problem
TSP_ACO_MMAS
- 用蚁群算法求解旅行商问题，城市数量为50，迭代500次-to solve tsp with ant
ACO
- TSP旅行商问题，解决城市旅行距离最短的问题，基于蚁群算法求解。-TSP traveling salesman problem, solve the problem of urban travel the shortest distance, based on ant colony algorithm.
ant1
- 使用著名的蚁群算法来求解城市旅行商的问题-Use well-known ant colony algorithm to solve the traveling salesman problem
acoa
- 使用著名的蚁群算法来求解城市旅行商的问题-Use well-known ant colony algorithm to solve the traveling salesman problem
chapter22
- 蚁群算法的优化计算——旅行商问题(TSP)优化(Optimization of ant colony algorithm -- traveling salesman problem (TSP) optimization)
TSP_Ant
- 通过蚁群算法，建立数学模型，从而解决旅行商问题(Through the ant colony algorithm, a mathematical model is established to solve the traveling salesman problem)
8593ec8fa2e2
- 有效解决旅行商问题，与遗传算法相互结合。优化路线(Solve the traveling salesman problem effectively)
蚁群算法实例
- 旅行商TSP问题蚁群算法在matlab平台的实现(TSP problem Ant Colony Algorithm)
智能优化算法及其matlab实例code
- 遗传算法 粒子群 蚁群等7种算法，可解决求极值问题以及旅行商问题(Genetic algorithm, particle swarm optimization and ant colony algorithm can solve extreme value problem and traveling salesman problem. 7 algorithms are used to solve extreme value problem and traveling salesman probl