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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. Artificial Ants stand for multi-agent methods inspired by the behavior of real ants.  

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. Artificial Ants stand for multi-agent methods inspired by the behavior of real ants.  

在计算机科学和运筹学中，蚁群优化算法(ACO)是一种用于解决计算问题的概率技术，它可以简化为通过图寻找好的路径。人工蚂蚁是受真蚂蚁行为启发的多智能体方法。

在计算机科学和运筹学中，<font color="#ff8000"> 蚁群优化算法ant colony optimization algorithm</font>(ACO)是一种用于解决计算问题的概率技术，它可以简化为通过图表来寻找好的路径。人工蚂蚁是受真蚂蚁行为启发的多智能体方法。

The pheromone-based communication of biological [[ant]]s is often the predominant paradigm used.<ref>{{cite book |last = Monmarché Nicolas, Guinand Frédéric and Siarry Patrick  |title = Artificial Ants |publisher = Wiley-ISTE  |year = 2010 |isbn = 978-1-84821-194-0}}</ref>  Combinations of Artificial Ants and [[local search (optimization)|local search]] algorithms have become a method of choice for numerous optimization tasks involving some sort of [[Graph (discrete mathematics)|graph]], e.g., [[vehicle routing problem|vehicle routing]] and internet [[routing]]. The burgeoning activity in this field has led to conferences dedicated solely to Artificial Ants, and to numerous commercial applications by specialized companies such as [[AntOptima]].

The pheromone-based communication of biological [[ant]]s is often the predominant paradigm used.<ref>{{cite book |last = Monmarché Nicolas, Guinand Frédéric and Siarry Patrick  |title = Artificial Ants |publisher = Wiley-ISTE  |year = 2010 |isbn = 978-1-84821-194-0}}</ref>  Combinations of Artificial Ants and [[local search (optimization)|local search]] algorithms have become a method of choice for numerous optimization tasks involving some sort of [[Graph (discrete mathematics)|graph]], e.g., [[vehicle routing problem|vehicle routing]] and internet [[routing]]. The burgeoning activity in this field has led to conferences dedicated solely to Artificial Ants, and to numerous commercial applications by specialized companies such as [[AntOptima]].

As an example, Ant colony optimization  is a class of optimization algorithms modeled on the actions of an ant colony.  Artificial 'ants' (e.g. simulation agents) locate optimal solutions by moving through a parameter space representing all possible solutions.  Real ants lay down pheromones directing each other to resources while exploring their environment.  The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions.  One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey bee, another social insect.

As an example, Ant colony optimization  is a class of optimization algorithms modeled on the actions of an ant colony.  Artificial 'ants' (e.g. simulation agents) locate optimal solutions by moving through a parameter space representing all possible solutions.  Real ants lay down pheromones directing each other to resources while exploring their environment.  The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions.  One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey bee, another social insect.

以蚁群算法为例，提出了一类以蚁群行为为模型的优化算法。人造「蚂蚁」(例如:。模拟代理)通过移动的参数空间代表所有可能的解决方案定位最优解。真正的蚂蚁在探索自己的环境时，会产生信息素，互相指引对方寻找资源。模拟的“蚂蚁”类似地记录它们的位置和解决方案的质量，这样在随后的模拟迭代中，更多的蚂蚁找到更好的解决方案。这种方法的一个变种是蜜蜂算法，它更类似于另一种社会昆虫---- 蜜蜂的觅食模式。

以蚁群算法为例，它是一种提出了一类以蚁群行为为模型的优化算法。人造「蚂蚁」(例如:模拟代理)通过移动的参数空间代表所有可能的解决方案定位最优解。真正的蚂蚁在探索自己的环境时，会产生信息素，互相指引对方寻找资源。模拟的“蚂蚁”类似地记录它们的位置和解决方案的质量，这样在随后的模拟迭代中，更多的蚂蚁找到更好的解决方案。这种方法的一个变种是蜜蜂算法，它更类似于另一种社会昆虫---- 蜜蜂的觅食模式。

==Overview==

==Overview==

 

In the natural world, ants of some species (initially) wander randomly,  and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but instead to follow the trail, returning and reinforcing it if they eventually find food (see Ant communication).

In the natural world, ants of some species (initially) wander randomly,  and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but instead to follow the trail, returning and reinforcing it if they eventually find food (see Ant communication).

在自然世界中，一些物种的蚂蚁(最初)随机徘徊，当发现食物回到它们的群落，同时产生信息素的踪迹。如果其他蚂蚁找到了这样一条路径，它们很可能不会继续随机行走，而是沿着这条路径前进，如果它们最终找到了食物，就会返回并加固这条路径(参见蚂蚁通讯)。

在自然世界中，一些物种的蚂蚁(最初)随机徘徊，一发现食物便回到它们的群落，同时产生信息素的踪迹。如果其他蚂蚁找到了这样一条路径，它们很可能不会继续随机行走，而是沿着这条路径前进，如果它们最终找到了食物，就会返回并加固这条路径(参见蚂蚁通讯)。

===Ambient networks of intelligent objects===

===Ambient networks of intelligent objects===

 

New concepts are required since “intelligence” is no longer centralized but can be found throughout all minuscule objects. Anthropocentric concepts have been known to lead to the production of IT systems in which data processing, control units and calculating forces are centralized. These centralized units have continually increased their performance and can be compared to the human brain. The model of the brain has become the ultimate vision of computers. [[Ambient networks]] of intelligent objects and, sooner or later, a new generation of information systems which are even more diffused and based on nanotechnology, will profoundly change this concept. Small devices that can be compared to insects do not dispose of a high intelligence on their own. Indeed, their intelligence can be classed as fairly limited. It is, for example, impossible to integrate a high performance calculator with the power to solve any kind of mathematical problem into a biochip that is implanted into the human body or integrated in an intelligent tag which is designed to trace commercial articles. However, once those objects are interconnected they dispose of a form of intelligence that can be compared to a colony of ants or bees. In the case of certain problems, this type of intelligence can be superior to the reasoning of a centralized system similar to the brain.<ref name="Waldner 2008 214">{{cite book |last = Waldner  |first = Jean-Baptiste  |authorlink = Jean-Baptiste Waldner  |title = Nanocomputers and Swarm Intelligence |publisher = [[ISTE Ltd|ISTE]] John Wiley & Sons |place = London |year = 2008 |isbn = 978-1-84704-002-2  | page = 214}}</ref>

New concepts are required since “intelligence” is no longer centralized but can be found throughout all minuscule objects. Anthropocentric concepts have been known to lead to the production of IT systems in which data processing, control units and calculating forces are centralized. These centralized units have continually increased their performance and can be compared to the human brain. The model of the brain has become the ultimate vision of computers. [[Ambient networks]] of intelligent objects and, sooner or later, a new generation of information systems which are even more diffused and based on nanotechnology, will profoundly change this concept. Small devices that can be compared to insects do not dispose of a high intelligence on their own. Indeed, their intelligence can be classed as fairly limited. It is, for example, impossible to integrate a high performance calculator with the power to solve any kind of mathematical problem into a biochip that is implanted into the human body or integrated in an intelligent tag which is designed to trace commercial articles. However, once those objects are interconnected they dispose of a form of intelligence that can be compared to a colony of ants or bees. In the case of certain problems, this type of intelligence can be superior to the reasoning of a centralized system similar to the brain.<ref name="Waldner 2008 214">{{cite book |last = Waldner  |first = Jean-Baptiste  |authorlink = Jean-Baptiste Waldner  |title = Nanocomputers and Swarm Intelligence |publisher = [[ISTE Ltd|ISTE]] John Wiley & Sons |place = London |year = 2008 |isbn = 978-1-84704-002-2  | page = 214}}</ref>

New concepts are required since “intelligence” is no longer centralized but can be found throughout all minuscule objects. Anthropocentric concepts have been known to lead to the production of IT systems in which data processing, control units and calculating forces are centralized. These centralized units have continually increased their performance and can be compared to the human brain. The model of the brain has become the ultimate vision of computers. Ambient networks of intelligent objects and, sooner or later, a new generation of information systems which are even more diffused and based on nanotechnology, will profoundly change this concept. Small devices that can be compared to insects do not dispose of a high intelligence on their own. Indeed, their intelligence can be classed as fairly limited. It is, for example, impossible to integrate a high performance calculator with the power to solve any kind of mathematical problem into a biochip that is implanted into the human body or integrated in an intelligent tag which is designed to trace commercial articles. However, once those objects are interconnected they dispose of a form of intelligence that can be compared to a colony of ants or bees. In the case of certain problems, this type of intelligence can be superior to the reasoning of a centralized system similar to the brain.

New concepts are required since “intelligence” is no longer centralized but can be found throughout all minuscule objects. Anthropocentric concepts have been known to lead to the production of IT systems in which data processing, control units and calculating forces are centralized. These centralized units have continually increased their performance and can be compared to the human brain. The model of the brain has become the ultimate vision of computers. Ambient networks of intelligent objects and, sooner or later, a new generation of information systems which are even more diffused and based on nanotechnology, will profoundly change this concept. Small devices that can be compared to insects do not dispose of a high intelligence on their own. Indeed, their intelligence can be classed as fairly limited. It is, for example, impossible to integrate a high performance calculator with the power to solve any kind of mathematical problem into a biochip that is implanted into the human body or integrated in an intelligent tag which is designed to trace commercial articles. However, once those objects are interconnected they dispose of a form of intelligence that can be compared to a colony of ants or bees. In the case of certain problems, this type of intelligence can be superior to the reasoning of a centralized system similar to the brain.

需要新的概念，因为“智能”不再是集中的，而是可以在所有微小的物体中找到。以人为中心的概念已经被认为导致了 IT 系统的产生，其中数据处理、控制单元和计算力量是集中的。这些集中的单位不断地提高他们的表现，可以与人类的大脑相比较。大脑模型已经成为计算机的终极愿景。由智能物体构成的环境网络，以及基于纳米技术的新一代信息系统，迟早会深刻地改变这一概念。可以与昆虫相比较的小型设备本身并不具备高智能。事实上，他们的智力可以归类为相当有限。例如，不可能将一个有能力解决任何数学问题的高性能计算器集成到植入人体的生物芯片中，或者集成到一个用于跟踪商业文章的智能标签中。然而，一旦这些物体互相连接起来，它们就拥有了一种可以与一群蚂蚁或蜜蜂相提并论的智慧。在某些问题的情况下，这种类型的智能可能优于类似于大脑的集中式系统的推理。

我们需要新的概念，因为“智能”不再是集中的，而是可以在所有微小的物体中找到。以人为中心的概念已经被认为导致了 IT 系统的产生，其中数据处理、控制单元和计算力量是集中的。这些集中的单位不断地提高他们的表现，可以与人类的大脑相比较。大脑模型已经成为计算机的终极愿景。由智能物体构成的环境网络，以及基于纳米技术的新一代信息系统，迟早会深刻地改变这一概念。可以与昆虫相比较的小型设备本身并不具备高智能。事实上，他们的智力可以归类为相当有限。例如，不可能将一个有能力解决任何数学问题的高性能计算器集成到植入人体的生物芯片中，或者集成到一个用于跟踪商业文章的智能标签中。然而，一旦这些物体互相连接起来，它们就拥有了一种可以与一群蚂蚁或蜜蜂相提并论的智慧。在某些问题的情况下，这种类型的智能可能优于类似大脑的集中式系统的推理。

===Artificial pheromone system===

===Artificial pheromone system===

 

Pheromone-based communication is one of the most effective ways of communication which is widely observed in nature. Pheromone is used by social insects such as

Pheromone-based communication is one of the most effective ways of communication which is widely observed in nature. Pheromone is used by social insects such as

Using projected light was presented in an 2007 IEEE paper by Garnier, Simon, et al. as an experimental setup to study pheromone-based communication with micro autonomous robots. Another study that proposed a novel pheromone communication method, COSΦ, for a swarm robotic system is based on precise and fast visual localization.  

Using projected light was presented in an 2007 IEEE paper by Garnier, Simon, et al. as an experimental setup to study pheromone-based communication with micro autonomous robots. Another study that proposed a novel pheromone communication method, COSΦ, for a swarm robotic system is based on precise and fast visual localization.  

2007年，由 Garnier，Simon 等人在 IEEE 的一篇论文中提出了使用投射光。作为研究信息素与微型自主机器人通信的实验装置。另一项针对群机器人系统的研究提出了一种新颖的信息素通信方法 cosφ，该方法基于精确快速的视觉定位。

2007年，由 Garnier，Simon 等人在 IEEE 的一篇论文中提出了使用投射光作为研究信息素与微型自主机器人通信的实验装置。另一项针对群机器人系统的研究提出了一种新颖的信息素通信方法 cosφ，该方法基于精确快速的视觉定位。

The system allows simulation of a virtually unlimited number of different pheromones and provides the result of their interaction as a gray-scale image on a horizontal LCD screen that the robots move on. In order to demonstrate the pheromone communication method, Colias autonomous micro robot was deployed as the swarm robotic platform.{{Citation needed|date=December 2019|reason=removed citation to predatory publisher content}}

The system allows simulation of a virtually unlimited number of different pheromones and provides the result of their interaction as a gray-scale image on a horizontal LCD screen that the robots move on. In order to demonstrate the pheromone communication method, Colias autonomous micro robot was deployed as the swarm robotic platform.{{Citation needed|date=December 2019|reason=removed citation to predatory publisher content}}

The system allows simulation of a virtually unlimited number of different pheromones and provides the result of their interaction as a gray-scale image on a horizontal LCD screen that the robots move on. In order to demonstrate the pheromone communication method, Colias autonomous micro robot was deployed as the swarm robotic platform.

The system allows simulation of a virtually unlimited number of different pheromones and provides the result of their interaction as a gray-scale image on a horizontal LCD screen that the robots move on. In order to demonstrate the pheromone communication method, Colias autonomous micro robot was deployed as the swarm robotic platform.

该系统可以模拟几乎无限数量的不同信息素，并在机器人移动的水平 LCD 屏幕上以灰度图像的形式提供它们相互作用的结果。为了演示信息素通信方法，以大肠杆菌自主微型机器人为群体机器人平台。

该系统可以模拟几乎无限数量的不同信息素，并在机器人移动的水平 LCD 屏幕上以灰度图像的形式提供它们相互作用的结果。为了演示信息素通信方法，自主微型机器人Colias调动为群体机器人平台。

==Algorithm and formulae==

==Algorithm and formulae==

 

In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the [[shortest path problem|shortest path]] on a weighted graph. In the first step of each iteration, each ant stochastically constructs a solution, i.e. the order in which the edges in the graph should be followed. In the second step, the paths found by the different ants are compared.  The last step consists of updating the pheromone levels on each edge.

In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the [[shortest path problem|shortest path]] on a weighted graph. In the first step of each iteration, each ant stochastically constructs a solution, i.e. the order in which the edges in the graph should be followed. In the second step, the paths found by the different ants are compared.  The last step consists of updating the pheromone levels on each edge.