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Description: 蚁群算法是模仿真实的蚁群行为而提出的一种模拟进化算法，蚂蚁之间是通过一种信息素的物质传递信息的，蚂蚁在运动的过程中能够在经过的路径上留下该物质，而且能够感知这种物质的存在及其强度，并以此指导自己的运动方向。它是一种通过类比组合优化和蚁群觅食行为而构造的一种算法，可以用来解决各种组合优化问题，本代码用来解决生产调度问题-Ant colony algorithm is based on real ant colony behavior and proposed a simulated evolutionary algorithm, ants are among the pheromone through a material and impart information, ants in the process of movement in the path on the left of the substance , but also perceived the existence of such material and its strength, and to guide their direction. By analogy it is a combinatorial optimization and ant colony foraging behavior and structure of an algorithm can be used to solve various combinatorial optimization problems, the code used to solve the production scheduling problem
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Author: 穆海华 |
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Description: 利用蚂蚁算法求解最短路径问题，稍作修改既可以用于移动机器人路径规划当中。-Ants use the shortest path algorithm to solve the problem, with some slight modifications can be used in mobile robot path planning among them.
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Author: 付他 |
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Description: 双蚁群算法实现机器人路径规划，采用面向对象的方法编写，蚂蚁数量和起始点可以设置，障碍率可以设置，障碍可以自动生成也可以手工使用鼠标设置。
已发表相关论文。
有兴趣的朋友可以检索“孙纯哲 凹形障碍全局路径规划的双蚁群完全交叉算法”-Ant colony algorithm to achieve dual-robot path planning, object-oriented method to prepare, ants can set the number and the starting point, the rate can be set up barriers, obstacles can be automatically generated using the mouse can also be set manually. Relevant papers have been published. Friends are interested in can be retrieved, " Sun Zhe pure concave obstacles dual global path planning algorithm ant colony completely cross"
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Author: sunchunzhe |
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Description: 详细介绍Matleb的蚁群算法的工具箱如何造作,不错的学习资料-Details of the ant colony algorithm Matleb how the toolbox acted and good learning materials
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Author: 周晓阳 |
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Description: 路径规划中群蚁优化算法的源代码，可通过修改程序自由设置障碍和地形-Group of ants in the path planning optimization algorithm source code, modify the program free by obstacles and terrain
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Author: 晴空 |
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Description: Ant algorithm with pheromone deposit. Ants devided into multiple ant colonies.
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Author: abia |
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Description: matlab中的蚂蚁算法又叫蚁群算法。主要解决函数优化，寻最优值问题-In matlab ，the ants can revolosion the problem about chath the rightes answer .
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Author: yangye |
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Description: 在状态转移规则的基础上，局部更新规则和全局更新的蚁群算法的规则，我们提出一个IM证明蚂蚁的最低成本的服务质量蚁群算法（QoS）的路径用于解决网络路由问题与延迟以及背包约束中进行模拟。约52.33％蚂蚁找到成功的QoS路由。并收敛到最佳。-based on the state transition rule，the local updating rule and the global updating rule of ant colony algorithm ，we propose an im proved ant colony algorithm of the least cost quality of service(QoS)unicast routing．The algorithm is used for solving the routing problem with delay．delay jitter．bandwidth，and packet lOSS constrained． In the simulation，about 52．33 ants find the Successful QoS routing ．and converge to the best． It is proved that the algorithm is efficient and effective．
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Author: lifei |
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Description: 蚁群算法解决TSP问题的MATLAB实现
出动m只蚂蚁，每只蚂蚁各随机选择一条路径，记为I=[1 2 3···m]，长度记为long(I)
计算出每条路径的信息素浓度，记为P(I)=1/long(I)，并进行归一化处理
重新出动m只蚂蚁，按如下规则选择路径：
每只蚂蚁都以一个概率p1选择新路径(路径随机)；
未选择新路径的蚂蚁以概率P(I)选择路径I；
所有蚂蚁都以一个小概率p2对自己的路径进行局部变化；
更新所有路径，计算出每条路径的信息素浓度；
重复上述步骤，直至仅剩一条路径。
-Ant colony algorithm to solve TSP problems MATLAB realization
Dispatched m ants, ants randomly select a path for each, denoted I = [1 2 3 · · · m], length denoted as long (I)
Calculated pheromone each path, denoted as P (I) = 1/long (I), and the normalization processing
Re-dispatched m ants, select the path according to the following rules:
Each ant with a probability p1 choose a new path (path random)
Ants do not select a new path with probability P (I) choose the path I
All ants are in a small probability p2 locally on their own path to change
Update all paths, each path is calculated pheromone
Repeat the above steps until the remaining path.
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Author: 水上飘 |
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Description: 首先实现一个ant蚂蚁类，用此蚂蚁类实现搜索。
算法按照tsp问题去解决，但是在最后计算路径的时候有区别。
比如有10个城市,城市1是配送站，蚂蚁搜索的得到的路径是1,3,5,9,4,10,2,6,8,7。
计算路径的时候把城市依次放入派送线路中,
每放入一个城市前，检查该城市放入后是否会超过车辆最大载重
如果没有超过就放入
如果超过，就重新开始一条派送路线
……
直到最后一个城市放完
就会得到多条派送路线
这样处理比较简单可以把vrp问题转为tsp问题求解
但是实际效果还需要验证。(First of all, a ant ant class is implemented, and the ant class is used to search.
The algorithm is solved according to the TSP problem, but there is a difference in the final calculation of the path.
For example, there are 10 cities, and 1 of cities are distribution stations. The path for ants to search is 1,3,5,9,4,10,2,6,8,7.
When calculating the path, place the city in the delivery route,
Before entering a city, check whether the city will exceed the maximum load of the vehicle after it is put in
If it's not over, put it in
If it's over, start a delivery route again
......
Until the last city is over
You'll get multiple delivery routes
This approach is relatively simple, you can change the VRP Problem to TSP problem solving
But the actual results need to be verified.)
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Author: Ywain
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Description: 蚂蚁算法
蚁群算法是一种用来寻找优化路径的概率型算法。它由Marco Dorigo于1992年在他的博士论文中提出，其灵感来源于蚂蚁在寻找食物过程中发现路径的行为。这种算法具有分布计算、信息正反馈和启发式搜索的特征，本质上是进化算法中的一种启发式全局优化算法。蚁群系统(Ant System或Ant Colony System)是由意大利学者Dorigo、Maniezzo等人于20世纪90年代首先提出来的。他们在研究蚂蚁觅食的过程中，发现单个蚂蚁的行为比较简单，但是蚁群整体却可以体现一些智能的行为。例如蚁群可以在不同的环境下，寻找最短到达食物源的路径。这是因为蚁群内的蚂蚁可以通过某种信息机制实现信息的传递。后又经进一步研究发现，蚂蚁会在其经过的路径上释放一种可以称之为“信息素”的物质，蚁群内的蚂蚁对“信息素”具有感知能力，它们会沿着“信息素”浓度较高路径行走，而每只路过的蚂蚁都会在路上留下“信息素”，这就形成一种类似正反馈的机制，这样经过一段时间后，整个蚁群就会沿着最短路径到达食物源了。(ant algorithm
Ant colony algorithm is a probabilistic algorithm used to find the optimal path. It was proposed by Marco Dorigo in his doctoral dissertation in 1992, inspired by the way ants find paths in the search for food. This algorithm has the characteristics of distributed computation, positive feedback of information and heuristic search. It is essentially a heuristic global optimization algorithm in evolutionary algorithm. Ant colony system (Ant System or Ant Colony System) was first proposed by Italy scholars Dorigo, Maniezzo and others in 1990s. They studied ants foraging process, found that the behavior of a single ant is relatively simple, but the ant colony as a whole can reflect some intelligent behavior.)
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Author: joyslee
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Description: 路径优化蚁群算法matlab程序‘
在求解该问题的蚂蚁算法中，每只蚂蚁是一个独立的用于构造路线的过程，若干蚂蚁过程之间通过自适应的信息素值来交换信息，合作求解，并不断优化。(Path optimization, ant colony algorithm, matlab program.
In the ant algorithm for solving this problem, each ant is an independent procedure for constructing the route, between a number of ants by adaptive pheromone to exchange information, cooperate to solve and optimize.)
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Author: hhhhhhyy
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Description: In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search and share some similarities with Estimation of Distribution Algorithm.
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Author: reyhooon
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Description: 首先实现一个ant蚂蚁类，用此蚂蚁类实现搜索。
算法按照tsp问题去解决，但是在最后计算路径的时候有区别。
比如有10个城市,城市1是配送站，蚂蚁搜索的得到的路径是1,3,5,9,4,10,2,6,8,7。
计算路径的时候把城市依次放入派送线路中,
每放入一个城市前，检查该城市放入后是否会超过车辆最大载重
如果没有超过就放入
如果超过，就重新开始一条派送路线
……
直到最后一个城市放完
就会得到多条派送路线
这样处理比较简单可以把vrp问题转为tsp问题求解
但是实际效果还需要验证。(First, we implement a ant ant class, and use this ant class to search.
The algorithm is solved according to the TSP problem, but there are differences when calculating the path at last.
For example, there are 10 cities, 1 of cities are distribution stations, and the path of ants search is 1,3,5,9,4,10,2,6,8,7.
When calculating the path, put the city into the delivery line in turn.
Before placing a city, check whether the city will exceed the maximum vehicle load after putting it into the city.
If it's not over, put it in
If it's over, start a new route.
......
Until the last city is finished
There will be multiple delivery routes
This process is relatively simple and can transform the VRP problem into TSP problem solving.
But the actual effect needs to be verified.)
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Size: 2048 |
Author: henrylalala |
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Description: 蚁群算法是一种用来寻找优化路径的概率型算法。它由Marco Dorigo于1992年在他的博士论文中提出，其灵感来源于蚂蚁在寻找食物过程中发现路径的行为。
这种算法具有分布计算、信息正反馈和启发式搜索的特征，本质上是进化算法中的一种启发式全局优化算法。蚁群算法的matlab实现(Ant colony algorithm is a probabilistic algorithm for finding optimal paths. It was put forward by Marco Dorigo in his doctoral dissertation in 1992, which is inspired by the behavior of ants in finding food in the process of finding food.
This algorithm has the characteristics of distributed computing, positive feedback and heuristic search. In essence, it is a heuristic global optimization algorithm in evolutionary algorithm. Matlab implementation of ant colony algorithm)
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Author: 那拍拍 |
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