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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<ref>{{cite journal|last = Dorigo, Gambardella |first = M, L.M. |authorlink = M. Dorigo & L. M. Gambardella |title = Learning Approach to the Traveling Salesman Problem  |publisher = IEEE Transactions on Evolutionary Computation, 1 (1) |page=214 |year = 1997}}</ref>为例，它是一种模拟蚁群行为的优化算法。人造”蚂蚁“（例如:仿真模拟的主体）通过在代表所有可能解的参数空间移动来求取最优解。真实蚂蚁们在探索环境时，会产生<font color="#ff8000">信息素</font>来指引彼此寻找资源。模拟的“蚂蚁”会类似地记录它们的位置和求解结果的好坏，以便在随后的仿真迭代计算中有更多的蚂蚁找到更好的解。<ref>Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. {{ISBN|0-262-04219-3}}</ref>蚁群算法的一个变种是蜜蜂算法，它更类似于另一种社会性昆虫——蜜蜂的觅食模式。

以蚁群算法<ref>{{cite journal|last = Dorigo, Gambardella |first = M, L.M. |authorlink = M. Dorigo & L. M. Gambardella |title = Learning Approach to the Traveling Salesman Problem  |publisher = IEEE Transactions on Evolutionary Computation, 1 (1) |page=214 |year = 1997}}</ref>为例，它是一种模拟蚁群行为的优化算法。人造”蚂蚁“（例如:仿真模拟的主体）通过在代表所有可能解的参数空间移动来求取最优解。真实蚂蚁们在探索环境时，会产生<font color="#ff8000">信息素 pheromone</font>来指引彼此寻找资源。模拟的“蚂蚁”会类似地记录它们的位置和求解结果的好坏，以便在随后的仿真迭代计算中有更多的蚂蚁找到更好的解。<ref>Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. {{ISBN|0-262-04219-3}}</ref>蚁群算法的一个变种是蜜蜂算法，它更类似于另一种社会性昆虫——蜜蜂的觅食模式。

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 shares some similarities with estimation of distribution algorithms.

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 shares some similarities with estimation of distribution algorithms.

在<font color="#ff8000">群智能</font>方法中,这个算法是蚁群算法家族的一员。它构成了某些<font color="#ff8000">元启发式</font>的优化。第一个蚁群算法最初由 Marco Dorigo 在1992年的博士论文中提出<ref>A. Colorni, M. Dorigo et V. Maniezzo, ''Distributed Optimization by Ant Colonies'', actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.</ref><ref name="M. Dorigo, Optimization, Learning and Natural Algorithms">M. Dorigo, ''Optimization, Learning and Natural Algorithms'', PhD thesis, Politecnico di Milano, Italy, 1992.</ref>，目的是基于蚂蚁在它们的聚居地和食物之间寻找一条路径的行为，在一张图中寻找最佳路径。借鉴了蚂蚁行为的各个方面，最初的蚁群算法已经变得多样化以解决更多类型的数值问题，并且也出现了一些问题。从更高的角度来看，蚁群算法执行基于模型的搜索<ref>{{cite journal|last1=Zlochin|first1=Mark|last2=Birattari|first2=Mauro|last3=Meuleau|first3=Nicolas|last4=Dorigo|first4=Marco|title=Model-Based Search for Combinatorial Optimization: A Critical Survey|journal=Annals of Operations Research|date=1 October 2004|volume=131|issue=1–4|pages=373–395|doi=10.1023/B:ANOR.0000039526.52305.af|language=en|issn=0254-5330|citeseerx=10.1.1.3.427|s2cid=63137}}</ref> ，并与分布估计算法具有一定的相似性。

在<font color="#ff8000">群智能 swarm intelligence</font>方法中,这个算法是蚁群算法家族的一员。它构成了某些<font color="#ff8000">元启发式 metaheuristic</font>的优化。第一个蚁群算法最初由 Marco Dorigo 在1992年的博士论文中提出<ref>A. Colorni, M. Dorigo et V. Maniezzo, ''Distributed Optimization by Ant Colonies'', actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.</ref><ref name="M. Dorigo, Optimization, Learning and Natural Algorithms">M. Dorigo, ''Optimization, Learning and Natural Algorithms'', PhD thesis, Politecnico di Milano, Italy, 1992.</ref>，目的是基于蚂蚁在它们的聚居地和食物之间寻找一条路径的行为，在一张图中寻找最佳路径。借鉴了蚂蚁行为的各个方面，最初的蚁群算法已经变得多样化以解决更多类型的数值问题，并且也出现了一些问题。从更高的角度来看，蚁群算法执行基于模型的搜索<ref>{{cite journal|last1=Zlochin|first1=Mark|last2=Birattari|first2=Mauro|last3=Meuleau|first3=Nicolas|last4=Dorigo|first4=Marco|title=Model-Based Search for Combinatorial Optimization: A Critical Survey|journal=Annals of Operations Research|date=1 October 2004|volume=131|issue=1–4|pages=373–395|doi=10.1023/B:ANOR.0000039526.52305.af|language=en|issn=0254-5330|citeseerx=10.1.1.3.427|s2cid=63137}}</ref> ，并与分布估计算法具有一定的相似性。

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.

因为“智能”不再是集中的而可以在所有微小的物体中找到，所以需要引入一些新的概念。以人为中心的概念被认为引导了 IT 系统的产生，其中数据处理、控制单元和算力是集中的。这些集中的单元功能不断地提高，并可以与人类大脑相比较。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<font color="#ff8000">纳米技术</font>的新一代信息系统，迟早会深刻地改变这种概念。可以与昆虫相比的小型设备本身并不具备高智能。事实上，他们的智能程度相当有限。例如，不可能把一个能解决任何数学问题的高性能计算器集成到可以植入人体的生物芯片中，或者集成到一个用于跟踪商品的智能标签中。然而，一旦这些物体互相连接起来，它们就拥有了可以与一群蚂蚁或蜜蜂相提并论的智慧。在某些问题下，这种类型的智能可能优于类似于大脑的集中系统的推理<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>。

因为“智能”不再是集中的而可以在所有微小的物体中找到，所以需要引入一些新的概念。以人为中心的概念被认为引导了 IT 系统的产生，其中数据处理、控制单元和算力是集中的。这些集中的单元功能不断地提高，并可以与人类大脑相比较。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<font color="#ff8000">纳米技术 nanotechnology</font>的新一代信息系统，迟早会深刻地改变这种概念。可以与昆虫相比的小型设备本身并不具备高智能。事实上，他们的智能程度相当有限。例如，不可能把一个能解决任何数学问题的高性能计算器集成到可以植入人体的生物芯片中，或者集成到一个用于跟踪商品的智能标签中。然而，一旦这些物体互相连接起来，它们就拥有了可以与一群蚂蚁或蜜蜂相提并论的智慧。在某些问题下，这种类型的智能可能优于类似于大脑的集中系统的推理<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>。

Nature offers several examples of how minuscule organisms, if they all follow the same basic rule, can create a form of collective intelligence on the macroscopic level. Colonies of social insects perfectly illustrate this model which greatly differs from human societies. This model is based on the co-operation of independent units with simple and unpredictable behavior. They move through their surrounding area to carry out certain tasks and only possess a very limited amount of information to do so. A colony of ants, for example, represents numerous qualities that can also be applied to a network of ambient objects. Colonies of ants have a very high capacity to adapt themselves to changes in the environment as well as an enormous strength in dealing with situations where one individual fails to carry out a given task. This kind of flexibility would also be very useful for mobile networks of objects which are perpetually developing. Parcels of information that move from a computer to a digital object behave in the same way as ants would do. They move through the network and pass from one knot to the next with the objective of arriving at their final destination as quickly as possible.

Nature offers several examples of how minuscule organisms, if they all follow the same basic rule, can create a form of collective intelligence on the macroscopic level. Colonies of social insects perfectly illustrate this model which greatly differs from human societies. This model is based on the co-operation of independent units with simple and unpredictable behavior. They move through their surrounding area to carry out certain tasks and only possess a very limited amount of information to do so. A colony of ants, for example, represents numerous qualities that can also be applied to a network of ambient objects. Colonies of ants have a very high capacity to adapt themselves to changes in the environment as well as an enormous strength in dealing with situations where one individual fails to carry out a given task. This kind of flexibility would also be very useful for mobile networks of objects which are perpetually developing. Parcels of information that move from a computer to a digital object behave in the same way as ants would do. They move through the network and pass from one knot to the next with the objective of arriving at their final destination as quickly as possible.

大自然中存在很多例子，说明了微小的生物在遵循同样的基本规则时，是如何创造出一种宏观层面的集体智慧的。社会性昆虫的群落完美地说明了这个与人类社会有很大不同的模型。该模型基于具有简单和不可预测行为的独立单元之间的合作。<ref>{{cite book|last = Waldner  |first = Jean-Baptiste  |authorlink = Jean-Baptiste Waldner  |title = Inventer l'Ordinateur du XXIème Siècle |publisher = [[Hermes Science]] |place = London |year = 2007  | pages = 259–265 |isbn = 978-2-7462-1516-0}}</ref> 他们穿过周围地区执行某些任务，但只掌握了非常有限的信息。例如，一群蚂蚁代表了<font color="#32CD32">许多可以应用到环境对象的网络中的特性</font>。蚂蚁群体具有很高的适应环境变化的能力，并且有很强的能力处理某项个体无法完成的任务。这种灵活性对于不断发展的移动网络也是非常有用的,从一台计算机到一个数字化对象的信息包和蚂蚁的行为一样。它们穿过网络并从一个节点传到下一个节点，目的是尽快到达终点。<ref>{{cite book |last = Waldner  |first = Jean-Baptiste  |authorlink = Jean-Baptiste Waldner  |title = Nanocomputers and Swarm Intelligence |publisher = ISTE John Wiley & Sons |place = London |year = 2008 |isbn = 978-1-84704-002-2  | page = 215}}</ref>

大自然中存在很多例子，说明了微小的生物在遵循同样的基本规则时，是如何创造出一种宏观层面的集体智慧的。社会性昆虫的群落完美地说明了这个与人类社会有很大不同的模型。该模型基于具有简单和不可预测行为的独立单元之间的合作。<ref>{{cite book|last = Waldner  |first = Jean-Baptiste  |authorlink = Jean-Baptiste Waldner  |title = Inventer l'Ordinateur du XXIème Siècle |publisher = [[Hermes Science]] |place = London |year = 2007  | pages = 259–265 |isbn = 978-2-7462-1516-0}}</ref> 他们穿过周围地区执行某些任务，但只掌握了非常有限的信息。例如，一群蚂蚁具有许多可以应用到环境对象的网络中的特性。蚂蚁群体具有很高的适应环境变化的能力，并且有很强的能力处理某项个体无法完成的任务。这种灵活性对于不断发展的移动网络也是非常有用的,从一台计算机到一个数字化对象的信息包和蚂蚁的行为一样。它们穿过网络并从一个节点传到下一个节点，目的是尽快到达终点。<ref>{{cite book |last = Waldner  |first = Jean-Baptiste  |authorlink = Jean-Baptiste Waldner  |title = Nanocomputers and Swarm Intelligence |publisher = ISTE John Wiley & Sons |place = London |year = 2008 |isbn = 978-1-84704-002-2  | page = 215}}</ref>

 

===Artificial pheromone system===

===Artificial pheromone system===

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 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 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.

在蚁群算法中，人工蚂蚁是一种简单的<font color="#32CD32">计算代理</font>，可以为给定优化问题寻找最优解。为了应用蚁群算法，需要将优化问题转化为在加权图上寻找最短路径的问题。在每次迭代的第一步，每个蚂蚁随机构造一个解，即图中的边应遵循的顺序。在第二步中，比较不同蚂蚁发现的路径。最后一步是更新每个边上的信息素水平。

 

--[[用户:Vicky|Vicky]]（[[用户讨论:Vicky|讨论]]） computational agent 相比于 计算代理 翻译为 计算主体 是不是更恰当一些

It is a recursive form of ant system which divides the whole search domain into several sub-domains and solves the objective on these subdomains. The results from all the subdomains are compared and the best few of them are promoted for the next level. The subdomains corresponding to the selected results are further subdivided and the process is repeated until an output of desired precision is obtained. This method has been tested on ill-posed geophysical inversion problems and works well.

It is a recursive form of ant system which divides the whole search domain into several sub-domains and solves the objective on these subdomains. The results from all the subdomains are compared and the best few of them are promoted for the next level. The subdomains corresponding to the selected results are further subdivided and the process is repeated until an output of desired precision is obtained. This method has been tested on ill-posed geophysical inversion problems and works well.

它是蚂蚁系统的一种<font color="#ff8000">递归</font>形式，将整个搜索域划分为若干子域，并在这些子域上求解。<ref>Gupta, D.K.; Arora, Y.; Singh, U.K.; Gupta, J.P., "Recursive Ant Colony Optimization for estimation of parameters of a function," Recent Advances in Information Technology (RAIT), 2012 1st International Conference on , vol., no., pp.448-454, 15–17 March 2012</ref>对所有子域的结果进行比较，<font color="#32cd32">并将其中最好的几个子域提升到下一个等级</font>。与选定结果相对应的子区域被进一步细分，并重复这个过程，直到获得所需精度的输出。该方法在不适定地球物理反演问题中得到了验证，效果良好。<ref>Gupta, D.K.; Gupta, J.P.; Arora, Y.; Shankar, U., "[http://nsg.eage.org/publication/download/?publication=68286 Recursive ant colony optimization: a new technique for the estimation of function parameters from geophysical field data]," Near Surface Geophysics , vol. 11, no. 3, pp.325-339</ref>

它是蚂蚁系统的一种<font color="#ff8000">递归 recursive</font>形式，将整个搜索域划分为若干子域，并在这些子域上求解。<ref>Gupta, D.K.; Arora, Y.; Singh, U.K.; Gupta, J.P., "Recursive Ant Colony Optimization for estimation of parameters of a function," Recent Advances in Information Technology (RAIT), 2012 1st International Conference on , vol., no., pp.448-454, 15–17 March 2012</ref>对所有子域的结果进行比较，<font color="#32cd32">并将其中最好的几个子域提升到下一个等级</font>。与选定结果相对应的子区域被进一步细分，并重复这个过程，直到获得所需精度的输出。该方法在不适定地球物理反演问题中得到了验证，效果良好。<ref>Gupta, D.K.; Gupta, J.P.; Arora, Y.; Shankar, U., "[http://nsg.eage.org/publication/download/?publication=68286 Recursive ant colony optimization: a new technique for the estimation of function parameters from geophysical field data]," Near Surface Geophysics , vol. 11, no. 3, pp.325-339</ref>

Ant colony optimization algorithms have been applied to many combinatorial optimization problems, ranging from quadratic assignment to protein folding or routing vehicles and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic problems, multi-targets and parallel implementations.

Ant colony optimization algorithms have been applied to many combinatorial optimization problems, ranging from quadratic assignment to protein folding or routing vehicles and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic problems, multi-targets and parallel implementations.

蚁群算法已经被应用于许多组合优化问题，从<font color="#ff8000">二次分配</font>到<font color="#ff8000">蛋白质折叠</font>或<font color="#ff8000">路径选择</font>问题，以及许多派生方法已经被应用于实变量的动态问题、随机问题、多目标和并行方法。

蚁群算法已经被应用于许多组合优化问题，从<font color="#ff8000">二次分配 quadratic assignment</font>到<font color="#ff8000">蛋白质折叠 protein folding</font>或<font color="#ff8000">路径选择 routing vehicles</font>问题，以及许多派生方法已经被应用于实变量的动态问题、随机问题、多目标和并行方法。

It has also been used to produce near-optimal solutions to the [[travelling salesman problem]]. They have an advantage over [[simulated annealing]] and [[genetic algorithm]] approaches of similar problems when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in [[network routing]] and urban transportation systems.

It has also been used to produce near-optimal solutions to the [[travelling salesman problem]]. They have an advantage over [[simulated annealing]] and [[genetic algorithm]] approaches of similar problems when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in [[network routing]] and urban transportation systems.

It has also been used to produce near-optimal solutions to the travelling salesman problem. They have an advantage over simulated annealing and genetic algorithm approaches of similar problems when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in network routing and urban transportation systems.

It has also been used to produce near-optimal solutions to the travelling salesman problem. They have an advantage over simulated annealing and genetic algorithm approaches of similar problems when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in network routing and urban transportation systems.

它也被用来产生<font color="#ff8000">旅行商问题</font>的近似最优解。当图形可能发生动态变化时，它们比<font color="#ff8000">模拟退火算法</font>和<font color="#ff8000">遗传算法</font>具有优势; 蚁群算法可以连续运行并实时适应变化。这是网络路由和城市交通系统中很感兴趣的内容。

它也被用来产生<font color="#ff8000">旅行商问题 travelling salesman problem</font>的近似最优解。当图形可能发生动态变化时，它们比<font color="#ff8000">模拟退火算法 simulated annealing</font>和<font color="#ff8000">遗传算法 genetic algorithm</font>具有优势; 蚁群算法可以连续运行并实时适应变化。这是网络路由和城市交通系统中很感兴趣的内容。

[[File:Knapsack ants.svg|thumb|[[Knapsack problem]]: The ants prefer the smaller drop of honey over the more abundant, but less nutritious, sugar]]

[[File:Knapsack ants.svg|thumb|[[Knapsack problem]]: The ants prefer the smaller drop of honey over the more abundant, but less nutritious, sugar]]