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蚁群优化算法 (查看源代码)

2020年11月3日 (二) 10:24的版本

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无编辑摘要

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=== Other applications ===

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其他应用

* [[Bankruptcy prediction]]<ref>{{cite journal|last1=Zhang|first1=Y.|title=A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm|journal=Mathematical Problems in Engineering|date=2013|volume=2013|page=753251|doi=10.1155/2013/753251|doi-access=free}}</ref>

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破产预测

* [[Classification]]<ref name="D. Martens, M pages 651">D. Martens, M. De Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens, "[https://ieeexplore.ieee.org/abstract/document/4336122/ Classification with Ant Colony Optimization]", IEEE Transactions on Evolutionary Computation, volume 11, number 5, pages 651—665, 2007.</ref>

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分类

* Connection-oriented [[network routing]]<ref>G. D. Caro and M. Dorigo, "Extending AntNet for best-effort quality-of-service routing," Proceedings of the First International Workshop on Ant Colony Optimization (ANTS’98), 1998.</ref>

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面向联系的网络路由

* Connectionless network routing<ref>G.D. Caro and M. Dorigo "[http://www.idsia.ch/~gianni/Papers/tech-rep-iridia-97-12.pdf AntNet: a mobile agents approach to adaptive routing]," Proceedings of the Thirty-First Hawaii International Conference on System Science, vol.7, pp.74-83, 1998.</ref><ref>G. D. Caro and M. Dorigo, "[https://www.researchgate.net/profile/Gianni_Di_Caro/publication/2328604_Two_Ant_Colony_Algorithms_For_Best-Effort_Routing_In_Datagram_Networks/links/0deec52909f32c7e6d000000/Two-Ant-Colony-Algorithms-For-Best-Effort-Routing-In-Datagram-Networks.pdf Two ant colony algorithms for best-effort routing in datagram networks]," Proceedings of the Tenth IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS’98), pp.541-546, 1998.</ref>

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无连接网络路由

* [[Data mining]]<ref name="D. Martens, M pages 651"/><ref>D. Martens, B. Baesens, T. Fawcett "[https://link.springer.com/content/pdf/10.1007/s10994-010-5216-5.pdf Editorial Survey: Swarm Intelligence for Data Mining]," Machine Learning, volume 82, number 1, pp. 1-42, 2011</ref><ref>R. S. Parpinelli, H. S. Lopes and A. A Freitas, "[http://neuro.bstu.by/ai/To-dom/My_research/Paper-0-again/For-courses/Ants/heuristic-dm-bk.pdf An ant colony algorithm for classification rule discovery]," Data Mining: A heuristic Approach, pp.191-209, 2002.</ref><ref>R. S. Parpinelli, H. S. Lopes and A. A Freitas, "[https://www.academia.edu/download/31181466/datamining070.pdf Data mining with an ant colony optimization algorithm]," IEEE Transactions on Evolutionary Computation, vol.6, no.4, pp.321-332, 2002.</ref>

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数据挖掘

* Discounted cash flows in project scheduling<ref>W. N. Chen, J. ZHANG and H. Chung, "[http://webdelprofesor.ula.ve/economia/gsfran/Asignaturas/EvaluacionFinEconProyec/2%20OptimizingDiscounted.pdf Optimizing Discounted Cash Flows in Project Scheduling--An Ant Colony Optimization Approach]", IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews Vol.40 No.5 pp.64-77, Jan. 2010.</ref>

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项目进度计划中的折现现金流

* [[distributed computing|Distributed]] [[information retrieval]]<ref>D. Picard, A. Revel, M. Cord, "An Application of Swarm Intelligence to Distributed Image Retrieval", Information Sciences, 2010</ref><ref>D. Picard, M. Cord, A. Revel, "[http://hal.upmc.fr/docs/00/65/63/63/PDF/manuscript.pdf Image Retrieval over Networks : Active Learning using Ant Algorithm]", IEEE Transactions on Multimedia, vol. 10, no. 7, pp. 1356--1365 - nov 2008</ref>

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分布式计算

* Energy and electricity network design<ref name="warner-and-vogel-2008">

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能源与电力网络设计

{{cite conference

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With an ACO algorithm, the shortest path in a graph, between two points A and B, is built from a combination of several paths. It is not easy to give a precise definition of what algorithm is or is not an ant colony, because the definition may vary according to the authors and uses. Broadly speaking, ant colony algorithms are regarded as populated metaheuristics with each solution represented by an ant moving in the search space. Ants mark the best solutions and take account of previous markings to optimize their search. They can be seen as probabilistic multi-agent algorithms using a probability distribution to make the transition between each iteration. In their versions for combinatorial problems, they use an iterative construction of solutions. According to some authors, the thing which distinguishes ACO algorithms from other relatives (such as algorithms to estimate the distribution or particle swarm optimization) is precisely their constructive aspect. In combinatorial problems, it is possible that the best solution eventually be found, even though no ant would prove effective. Thus, in the example of the Travelling salesman problem, it is not necessary that an ant actually travels the shortest route: the shortest route can be built from the strongest segments of the best solutions. However, this definition can be problematic in the case of problems in real variables, where no structure of 'neighbours' exists. The collective behaviour of social insects remains a source of inspiration for researchers. The wide variety of algorithms (for optimization or not) seeking self-organization in biological systems has led to the concept of "swarm intelligence", In practice, the use of an exchange of information between ants via the environment (a principle called "stigmergy") is deemed enough for an algorithm to belong to the class of ant colony algorithms. This principle has led some authors to create the term "value" to organize methods and behavior based on search of food, sorting larvae, division of labour and cooperative transportation.

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采用蚁群优化算法，由多条路径组合构造图中两点 a 和 b 之间的最短路径。要准确定义什么算法是蚁群算法，什么算法不是蚁群算法并不容易，因为蚁群算法的定义可能因作者和用途的不同而有所不同。广义地说，蚁群算法被认为是一种填充的元启发式算法，每个解由一个在搜索空间中移动的蚂蚁表示。蚂蚁标记最好的解决方案，并考虑到以前的标记，以优化他们的搜索。它们可以被看作是概率多智能体算法，使用概率分布进行每次迭代之间的转换。在他们解决组合问题的版本中，他们使用了迭代构造的解决方案。根据一些作者的观点，蚁群算法区别于其他相关算法(比如估计分布或粒子群优化的算法)正是蚁群算法的建设性方面。在组合问题中，最终可能找到最好的解决方案，即使没有蚂蚁被证明是有效的。因此，在旅行推销员问题的例子中，蚂蚁实际上并不需要走最短的路线: 最短的路线可以从最好的解决方案中最强的部分建立起来。然而，这一定义在实变量问题的情况下可能是有问题的，在实变量中没有“邻居”的结构存在。群居昆虫的集体行为仍然是研究人员的灵感来源。在生物系统中寻找自我组织的算法种类繁多(无论是优化还是非优化)已经导致了“群体智能”的概念，在实践中，蚂蚁之间通过环境交换信息的使用(一个被称为“暂时能力”的原则)被认为足以使算法属于蚁群算法的一类。这个原则促使一些作者创造了“价值”这个词来组织方法和行为，基于寻找食物，分类幼虫，分工和合作运输。

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采用蚁群优化算法，由多条路径组合构造图中两点 a 和 b 之间的最短路径。要准确定义什么算法是蚁群算法，什么算法不是蚁群算法并不容易，因为蚁群算法的定义可能因作者和用途的不同而有所不同。广义地说，蚁群算法被认为是一种填充的元启发式算法，每个解由一个在搜索空间中移动的蚂蚁表示。蚂蚁标记最好的解决方案，并考虑到以前的标记，以优化他们的搜索。它们可以被看作是概率多智能体算法，使用概率分布进行每次迭代之间的转换。在他们解决组合问题的版本中，他们使用了迭代构造的解决方案。根据一些作者的观点，蚁群算法区别于其他相关算法(比如估计分布或粒子群优化的算法)的正是蚁群算法的建设性方面。在组合问题中，最终可能找到最好的解决方案，即使没有蚂蚁被证明是有效的。因此，在旅行推销员问题的例子中，蚂蚁实际上并不需要走最短的路线: 最短的路线可以从最好的解决方案中最强的部分建立起来。然而，这一定义在实变量问题的情况下可能是有问题的，在实变量中没有“邻居”的结构存在。群居昆虫的集体行为仍然是研究人员的灵感来源。在生物系统中寻找自我组织的算法种类繁多(无论是优化还是非优化)已经导致了“群体智能”的概念，在实践中，蚂蚁之间通过环境交换信息的使用(一个被称为“暂时能力”的原则)被认为足以使算法属于蚁群算法的一类。这个原则促使一些作者创造了“价值”这个词来组织方法和行为，基于寻找食物，分类幼虫，分工和合作运输。

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* Grid workflow scheduling problem<ref>W. N. Chen and J. ZHANG "Ant Colony Optimization Approach to Grid Workflow Scheduling Problem with Various QoS Requirements", IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, Vol. 31, No. 1,pp.29-43,Jan 2009.</ref>

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网格工作流调度问题

* Inhibitory peptide design for [[protein protein interaction]]s<ref name=":0">{{Cite journal|last1=Zaidman|first1=Daniel|last2=Wolfson|first2=Haim J.|date=2016-08-01|title=PinaColada: peptide–inhibitor ant colony ad-hoc design algorithm|journal=Bioinformatics|volume=32|issue=15|pages=2289–2296|doi=10.1093/bioinformatics/btw133|pmid=27153578|issn=1367-4803|doi-access=free}}</ref>

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蛋白质-蛋白质相互作用的抑制肽设计

* Intelligent testing system<ref>Xiao. M.Hu, J. ZHANG, and H. Chung, "[https://ieeexplore.ieee.org/abstract/document/5061647/ An Intelligent Testing System Embedded with an Ant Colony Optimization Based Test Composition Method]", IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, Vol. 39, No. 6, pp. 659-669, Dec 2009.</ref>

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智能测试系统

* Power [[electronic circuit design]]<ref>J. ZHANG, H. Chung, W. L. Lo, and T. Huang, "[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.140.4340&rep=rep1&type=pdf Extended Ant Colony Optimization Algorithm for Power Electronic Circuit Design]", IEEE Transactions on Power Electronic. Vol.24,No.1, pp.147-162, Jan 2009.</ref>

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电源电子电路设计

* [[Protein folding]]<ref>X. M. Hu, J. ZHANG，J. Xiao and Y. Li, "[http://eprints.gla.ac.uk/5306/1/5306.pdf Protein Folding in Hydrophobic-Polar Lattice Model: A Flexible Ant- Colony Optimization Approach] ", Protein and Peptide Letters, Volume 15, Number 5, 2008, Pp. 469-477.</ref><ref>A. Shmygelska, R. A. Hernández and H. H. Hoos, "[ftp://nozdr.ru/biblio/kolxo3/Cs/CsLn/Ant%20Algorithms,%203%20conf.,%20ANTS%202002(LNCS2463,%20Springer,%202002)(ISBN%203540441468)(318s).pdf#page=54 An ant colony optimization algorithm for the 2D HP protein folding problem]{{Dead link|date=June 2020 |bot=InternetArchiveBot |fix-attempted=yes }}," Proceedings of the 3rd International Workshop on Ant Algorithms/ANTS 2002, Lecture Notes in Computer Science, vol.2463, pp.40-52, 2002.</ref><ref>{{cite book |author1=M. Nardelli |author2=L. Tedesco |author3=A. Bechini |title= Cross-lattice behavior of general ACO folding for proteins in the HP model |journal= Proc. Of ACM SAC 2013|year=2013|pages=1320–1327 |doi= 10.1145/2480362.2480611|isbn=9781450316569 |s2cid=1216890 |url=https://zenodo.org/record/3445092 }}</ref>

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蛋白质折叠

The inventors are Frans Moyson and Bernard Manderick. Pioneers of the field include Marco Dorigo, Luca Maria Gambardella.

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发明者是 Frans Moyson 和 Bernard ~~Manderick。这一领域的先驱包括马可 · 多里戈、卢卡 · 玛利亚 · 甘巴德拉。~~

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发明者是 Frans Moyson 和 Bernard Manderick。这一领域的先驱包括 Marco Dorigo, Luca Maria Gambardell。

* System identification<ref>L. Wang and Q. D. Wu, "Linear system parameters identification based on ant system algorithm," Proceedings of the IEEE Conference on Control Applications, pp. 401-406, 2001.</ref><ref>K. C. Abbaspour, R. Schulin, M. T. Van Genuchten, "[https://www.ars.usda.gov/arsuserfiles/20360500/pdf_pubs/P1797.pdf Estimating unsaturated soil hydraulic parameters using ant colony optimization]," Advances In Water Resources, vol. 24, no. 8, pp. 827-841, 2001.</ref>

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系统标识

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==Definition difficulty==

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定义困难那

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With an ACO algorithm, the shortest path in a graph, between two points A and B, is built from a combination of several paths.<ref>{{Cite journal | doi=10.1186/1471-2105-6-30| pmid=15710037| pmc=555464|year = 2005|last1 = Shmygelska|first1 = Alena| title=An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem| journal=BMC Bioinformatics| volume=6| pages=30| last2=Hoos| first2=Holger H.}}</ref> It is not easy to give a precise definition of what algorithm is or is not an ant colony, because the definition may vary according to the authors and uses. Broadly speaking, ant colony algorithms are regarded as [[people|populated]] [[metaheuristics]] with each solution represented by an ant moving in the search space.<ref>Fred W. Glover,Gary A. Kochenberger, ''Handbook of Metaheuristics'', [https://books.google.com/books?id=P-HpBwAAQBAJ&pg=PA276&lpg=PA276&dq=aco+algorithms+with+guaranteed+convergence+to+the+optimal+solution+metaheuristics&source=bl&ots=4kyU_bZLpg&sig=zSrzu89MRED00H8QWjixBMkw11k&hl=fr&sa=X&ved=0ahUKEwjm4_2ysurTAhUHZ1AKHabGAZIQ6AEIODAE#v=onepage&q=aco%20algorithms%20with%20guaranteed%20convergence%20to%20the%20optimal%20solution%20metaheuristics&f=false], Springer (2003)</ref> Ants mark the best solutions and take account of previous markings to optimize their search. They can be seen as [[probabilistic]] [[multi-agent]] algorithms using a [[probability distribution]] to make the transition between each [[iteration]].<ref>http://www.multiagent.fr/extensions/ICAPManager/pdf/LauriCharpillet2006.pdf</ref> In their versions for combinatorial problems, they use an iterative construction of solutions.<ref>WJ Gutjahr , ''ACO algorithms with guaranteed convergence to the optimal solution'', [https://homes.di.unimi.it/cordone/courses/2016-ae/Lez07-Materiali/ACOAlgoithmsWithGuaranteedConvergenceToTheOptimalSolution.pdf], (2002)</ref> According to some authors, the thing which distinguishes ACO algorithms from other relatives (such as algorithms to estimate the distribution or particle swarm optimization) is precisely their constructive aspect. In combinatorial problems, it is possible that the best solution eventually be found, even though no ant would prove effective. Thus, in the example of the Travelling salesman problem, it is not necessary that an ant actually travels the shortest route: the shortest route can be built from the strongest segments of the best solutions. However, this definition can be problematic in the case of problems in real variables, where no structure of 'neighbours' exists. The collective behaviour of [[social insects]] remains a source of inspiration for researchers. The wide variety of algorithms (for optimization or not) seeking self-organization in biological systems has led to the concept of "[[swarm intelligence]]",<ref name="Waldner 2008 214"/> which is a very general framework in which ant colony algorithms fit.

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根据一些作者的观点，ACO算法与其他相关算法（如分布估计算法或粒子群优化算法）的区别正是它们的建设性方面。在组合问题中，即使没有蚂蚁被证明是有效的，也有可能最终找到最佳解。因此，在旅行商问题的例子中，蚂蚁实际上并不需要走最短的路线：最短的路线可以从最佳解的最强部分建立起来。然而，在实际变量中存在问题的情况下，这个定义可能会有问题，因为不存在“邻居”的结构。[[群居昆虫]的集体行为仍然是研究人员灵感的源泉。生物系统中寻求自组织的各种各样的算法（无论是优化还是非优化）导致了“群体智能”的概念

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==Stigmergy algorithms==

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Stigmergy算法

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There is in practice a large number of algorithms claiming to be "ant colonies", without always sharing the general framework of optimization by canonical ant colonies.<ref>Santpal Singh Dhillon , ''Ant Routing, Searching and Topology Estimation Algorithms for Ad Hoc Networks'', [https://books.google.com/books?id=j5fOJqhwcJoC&pg=PA33&dq=Stigmergy+algorithms&hl=fr&sa=X&ved=0ahUKEwjwjfaAtOrTAhWnLsAKHVPkCjYQ6AEIKTAB#v=onepage&q=Stigmergy%20algorithms&f=false], IOS Press, (2008)</ref> In practice, the use of an exchange of information between ants via the environment (a principle called "[[stigmergy]]") is deemed enough for an algorithm to belong to the class of ant colony algorithms. This principle has led some authors to create the term "value" to organize methods and behavior based on search of food, sorting larvae, division of labour and cooperative transportation.<ref>A. Ajith; G. Crina; R. Vitorino (éditeurs), ''Stigmergic Optimization'', Studies in Computational Intelligence , volume 31, 299 pages, 2006. {{ISBN|978-3-540-34689-0}}</ref>

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在实践中，蚂蚁之间通过环境交换信息（称为“[[stigmergy]]”的原理）被认为足以使算法属于蚁群算法。基于这种“合作性”的分类原则，作者提出了一些“合作式”的分类和运输方式。

Period = from:1985 till:2005

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*[[Genetic algorithm]]s (GA) maintain a pool of solutions rather than just one. The process of finding superior solutions mimics that of evolution, with solutions being combined or mutated to alter the pool of solutions, with solutions of inferior quality being discarded.

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基因算法（GA）维护一个解决方案池，而不仅仅是一个。寻找更好的解决方案的过程模仿了进化的过程，解决方案被组合或变异以改变解决方案池，劣质的解决方案被丢弃。

* An [[estimation of distribution algorithm]] (EDA) is an [[evolutionary algorithm]] that substitutes traditional reproduction operators by model-guided operators. Such models are learned from the population by employing machine learning techniques and represented as probabilistic graphical models, from which new solutions can be sampled<ref>{{cite book|last1=Pelikan|first1=Martin|last2=Goldberg|first2=David E.|last3=Cantú-Paz|first3=Erick|title=BOA: The Bayesian Optimization Algorithm|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1|date=1 January 1999|pages=525–532|url=http://dl.acm.org/citation.cfm?id=2933973|isbn=9781558606111|series=Gecco'99}}</ref><ref>{{cite book|last1=Pelikan|first1=Martin|title=Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms|date=2005|publisher=Springer|location=Berlin [u.a.]|isbn=978-3-540-23774-7|edition=1st}}</ref> or generated from guided-crossover.<ref>{{cite book|last1=Thierens|first1=Dirk|s2cid=28648829|chapter=The Linkage Tree Genetic Algorithm|journal=Parallel Problem Solving from Nature, PPSN XI|date=11 September 2010|pages=264–273|doi=10.1007/978-3-642-15844-5_27|language=en|isbn=978-3-642-15843-8}}</ref><ref>{{cite journal|last1=Martins|first1=Jean P.|last2=Fonseca|first2=Carlos M.|last3=Delbem|first3=Alexandre C. B.|title=On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem|journal=Neurocomputing|date=25 December 2014|volume=146|pages=17–29|doi=10.1016/j.neucom.2014.04.069}}</ref>

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*[[Simulated annealing]] (SA) is a related global optimization technique which traverses the search space by generating neighboring solutions of the current solution. A superior neighbor is always accepted. An inferior neighbor is accepted probabilistically based on the difference in quality and a temperature parameter. The temperature parameter is modified as the algorithm progresses to alter the nature of the search.

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模拟退火（SA）是一种相关的全局优化技术，它通过生成当前解的相邻解来遍历搜索空间。优越的邻居总是被接受的。根据质量和温度参数的差异概率地接受次邻居。随着算法的进展，温度参数会被修改，以改变搜索的性质

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* Reactive search optimization focuses on combining machine learning with optimization, by adding an internal feedback loop to self-tune the free parameters of an algorithm to the characteristics of the problem, of the instance, and of the local situation around the current solution.

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反应式搜索优化是将机器学习与优化相结合，通过添加一个内部反馈回路，根据问题、实例和当前解决方案周围的局部情况，对算法的自由参数进行自调整。

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*[[Tabu search]] (TS) is similar to simulated annealing in that both traverse the solution space by testing mutations of an individual solution. While simulated annealing generates only one mutated solution, tabu search generates many mutated solutions and moves to the solution with the lowest fitness of those generated. To prevent cycling and encourage greater movement through the solution space, a tabu list is maintained of partial or complete solutions. It is forbidden to move to a solution that contains elements of the tabu list, which is updated as the solution traverses the solution space.

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[[塔布研究]]（TS）类似于模拟退火，两者都通过测试单个解的突变来遍历解空间。模拟退火只产生一个变异解，禁忌搜索则产生许多变异解，并移到适应度最低的解。为了防止循环并鼓励在解决方案空间中进行更大的移动，保留了部分或完整解决方案的禁忌列表。禁止移动到包含禁忌列表元素的解决方案，该列表会随着解决方案遍历解决方案空间而更新

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*[[Artificial immune system]] (AIS) algorithms are modeled on vertebrate immune systems.

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[[人工免疫系统]]（AIS）算法是以脊椎动物免疫系统为模型的。

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==History==

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历史

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Chronology of ant colony optimization algorithms.

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蚁群优化算法年表

* 1959, [[Pierre-Paul Grassé]] invented the theory of [[stigmergy]] to explain the behavior of nest building in [[termites]];<ref>P.-P. Grassé, ''La reconstruction du nid et les coordinations inter-individuelles chez Belicositermes natalensis et Cubitermes sp. La théorie de la Stigmergie : Essai d’interprétation du comportement des termites constructeurs'', Insectes Sociaux, numéro 6, p. 41-80, 1959.</ref>

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1959年，[[Pierre Paul grasse]发明了[[stigmergy]]理论来解释[[白蚁]筑巢的行为

* 1983, Deneubourg and his colleagues studied the [[collective behavior]] of [[ants]];<ref>J.L. Denebourg, J.M. Pasteels et J.C. Verhaeghe, ''[https://pdfs.semanticscholar.org/caac/71608a5e6b0907a8a99bba30d72d9a304152.pdf Probabilistic Behaviour in Ants : a Strategy of Errors?]'', Journal of Theoretical Biology, numéro 105, 1983.</ref>

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1983年，Deneubourg和他的同事研究了[[蚂蚁]]的[[集体行为]]

* 1988, and Moyson Manderick have an article on '''self-organization''' among ants;<ref name="F. Moyson, B. Manderick">F. Moyson, B. Manderick, ''The collective behaviour of Ants : an Example of Self-Organization in Massive Parallelism'', Actes de AAAI Spring Symposium on Parallel Models of Intelligence, Stanford, Californie, 1988.</ref>

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1988年，Moyson Manderick发表了一篇关于蚂蚁“自组织”的文章

* 1989, the work of Goss, Aron, Deneubourg and Pasteels on the '''collective behavior of Argentine ants''', which will give the idea of ant colony optimization algorithms;<ref name="S. Goss">S. Goss, S. Aron, J.-L. Deneubourg et J.-M. Pasteels, ''[https://www.researchgate.net/profile/Serge_Aron/publication/301232811_Self-Organized_Shortcuts_in_the_Argentine_Ant/links/59967b5daca27283b11d9070/Self-Organized-Shortcuts-in-the-Argentine-Ant.pdf Self-organized shortcuts in the Argentine ant]'', Naturwissenschaften, volume 76, pages 579-581, 1989</ref>

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1989年，Goss，Aron，Deneubourg和pastels关于“阿根廷蚂蚁的集体行为”的研究，这将为蚁群优化算法提供思路

* 1989, implementation of a model of behavior for food by Ebling and his colleagues;<ref>M. Ebling, M. Di Loreto, M. Presley, F. Wieland, et D. Jefferson,''An Ant Foraging Model Implemented on the Time Warp Operating System'', Proceedings of the SCS Multiconference on Distributed Simulation, 1989</ref>

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1989年，Ebling 和他的同事们实施了一种食物行为模式

* 1991, M. Dorigo proposed the '''ant system''' in his doctoral thesis (which was published in 1992<ref name="M. Dorigo, Optimization, Learning and Natural Algorithms" />). A technical report extracted from the thesis and co-authored by V. Maniezzo and A. Colorni<ref>Dorigo M., V. Maniezzo et A. Colorni, ''Positive feedback as a search strategy'', rapport technique numéro 91-016, Dip. Elettronica, Politecnico di Milano, Italy, 1991</ref> was published five years later;<ref name="Ant system" />

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1991年，M.Dorigo在他的博士论文（发表于1992年）中提出了“蚂蚁系统”

* 1994, Appleby and Steward of British Telecommunications Plc published the first application to [[telecommunications]] networks<ref>Appleby, S. & Steward, S. Mobile software agents for control in telecommunications networks, BT Technol. J., 12(2):104–113, April 1994</ref>

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1994年，英国电信公司的Appleby和Steward发布了第一个应用于[[电信]]网络的应用程序

* 1995, Gambardella and Dorigo proposed '''ant-q''', <ref> L.M. Gambardella and M. Dorigo, "Ant-Q: a reinforcement learning approach to the traveling salesman problem", Proceedings of ML-95, Twelfth International Conference on Machine Learning, A. Prieditis and S. Russell (Eds.), Morgan Kaufmann, pp. 252–260, 1995 </ref> the preliminary version of ant colony system as first estension of ant system; <ref name="Ant system" />.

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1995年，Gambardella和Dorigo提议“蚂蚁-q”

* 1996, Gambardella and Dorigo proposed '''ant colony system''' <ref> L.M. Gambardella and M. Dorigo, "Solving Symmetric and Asymmetric TSPs by Ant Colonies", Proceedings of the IEEE Conference on Evolutionary Computation, ICEC96, Nagoya, Japan, May 20-22, pp. 622-627, 1996; </ref>

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1996年，Gambardella和Dorigo提议“蚁群系统”

* 1996, publication of the article on ant system;<ref name="Ant system" />

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1996年，发表关于蚂蚁系统的文章

* 1996, Hoos and Stützle invent the '''max-min ant system''';<ref name="T. Stützle et H.H. Hoos" />

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1996年，Hoos和Stützle发明了“最大最小蚂蚁系统”

* 1997, Dorigo and Gambardella proposed ant colony system hybridized with local search;<ref name="M. Dorigo et L.M. Gambardella" />

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1997年，Dorigo和Gambardella提出了结合局部搜索的蚁群系统

* 1997, Schoonderwoerd and his colleagues published an improved application to [[telecommunication]] networks;<ref>R. Schoonderwoerd, O. Holland, J. Bruten et L. Rothkrantz, ''[https://pdfs.semanticscholar.org/f09e/03c5d759c7ca04e443d496e23c981f1b4a5d.pdf Ant-based load balancing in telecommunication networks]'', Adaptive Behaviour, volume 5, numéro 2, pages 169-207, 1997</ref>

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1997年，Schoonderwoerd和他的同事发表了一个改进的应用程序到[[电信]]网络

* 1998, Dorigo launches first conference dedicated to the ACO algorithms;<ref>M. Dorigo, ''ANTS’ 98, From Ant Colonies to Artificial Ants : First International Workshop on Ant Colony Optimization, ANTS 98'', Bruxelles, Belgique, octobre 1998.</ref>

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1998年，Dorigo召开了第一次专门讨论ACO算法的会议

* 1998, Stützle proposes initial '''parallel implementations''';<ref>T. Stützle, ''Parallelization Strategies for Ant Colony Optimization'', Proceedings of PPSN-V, Fifth International Conference on Parallel Problem Solving from Nature, Springer-Verlag, volume 1498, pages 722-731, 1998.</ref>

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1998年，Stützle提出最初的“并行实现”

*1999, Gambardella, Taillard and Agazzi proposed ''' macs-vrptw''', first multi ant colony system applied to vehicle routing problems with time windows, <ref> L.M. Gambardella, E. Taillard, G. Agazzi, "MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows", In D. Corne, M. Dorigo and F. Glover, editors, New Ideas in Optimization, McGraw-Hill, London, UK, pp. 63-76, 1999.</ref>

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1999年，Gambardella、Taillard和Agazzi提出了macs-vrptw，这是第一个应用于带时间窗的车辆路径问题的多蚁群系统

* 1999, Bonabeau, Dorigo and Theraulaz publish a book dealing mainly with artificial ants<ref>É. Bonabeau, M. Dorigo et G. Theraulaz, ''Swarm intelligence'', Oxford University Press, 1999.</ref>

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1999年，博纳博、多里戈和塞拉兹出版了一本主要讨论人工蚂蚁的书。博纳博，M.Dorigo et G.Theraulaz，“群体智能”，牛津大学出版社

* 2000, special issue of the Future Generation Computer Systems journal on ant algorithms<ref>M. Dorigo , G. Di Caro et T. Stützle, ''[https://www.academia.edu/download/30765111/FGCS-Editorial-final.pdf Special issue on "Ant Algorithms]"'', Future Generation Computer Systems, volume 16, numéro 8, 2000</ref>

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2000年，《未来一代计算机系统杂志》关于蚂蚁算法的特刊

* 2000, first applications to the [[Scheduling algorithm|scheduling]], scheduling sequence and the [[constraint satisfaction|satisfaction of constraints]];

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2000年，首次应用于[[调度算法|调度]]，调度序列和[[约束满足|满足约束

* 2000, Gutjahr provides the first evidence of [[limit of a sequence|convergence]] for an algorithm of ant colonies<ref>W.J. Gutjahr, ''[http://iridia.ulb.ac.be/~mdorigo/ACO/downloads/ants5.pdf A graph-based Ant System and its convergence]'', Future Generation Computer Systems, volume 16, pages 873-888, 2000.</ref>

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2000年，Gutjahr为蚁群算法提供了[[序列极限|收敛性]]的第一个证据

* 2001, the first use of COA algorithms by companies ([http://www.eurobios.com/ Eurobios] and [http://www.antoptima.com/ AntOptima]);

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2001年，公司首次使用COA算法

* 2001, Iredi and his colleagues published the first '''multi-objective''' algorithm<ref>S. Iredi, D. Merkle et M. Middendorf, ''[https://link.springer.com/chapter/10.1007/3-540-44719-9_25 Bi-Criterion Optimization with Multi Colony Ant Algorithms]'', Evolutionary Multi-Criterion Optimization, First International Conference (EMO’01), Zurich, Springer Verlag, pages 359-372, 2001.</ref>

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2001年，Iredi和他的同事发表了第一个“多目标”算法

* 2002, first applications in the design of schedule, Bayesian networks;

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2002年，贝叶斯网络在进度计划设计中的首次应用

* 2002, Bianchi and her colleagues suggested the first algorithm for [[stochastic]] problem;<ref>L. Bianchi, L.M. Gambardella et M.Dorigo, ''[http://hcot.ir/wp-content/uploads/2015/03/An-Ant-Colony-Optimization-Approach-to-the-Probabilistic-Traveling-Salesman-Problem.pdf An ant colony optimization approach to the probabilistic traveling salesman problem]'', PPSN-VII, Seventh International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, Springer Verlag, Berlin, Allemagne, 2002.</ref>

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2002年，Bianchi和她的同事提出了[[随机]]问题的第一个算法；

* 2004, Dorigo and Stützle publish the Ant Colony Optimization book with MIT Press <ref>M. Dorigo and T. Stützle, ''Ant Colony Optimization'', MIT Press, 2004.</ref>

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2004年，Dorigo和Stützle与麻省理工学院出版社出版了《蚁群优化》一书

* 2004, Zlochin and Dorigo show that some algorithms are equivalent to the [[stochastic gradient descent]], the [[cross-entropy method]] and [[algorithms to estimate distribution]]<ref name="Zlochin model-based search"/>

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2004年，Zlochin和Dorigo证明了一些算法等价于[[随机梯度下降]、[[交叉熵方法]]和[[估计分布的算法]]

* 2005, first applications to [[protein folding]] problems.

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2005年，首次应用于[[蛋白质折叠]]问题

* 2012, Prabhakar and colleagues publish research relating to the operation of individual ants communicating in tandem without pheromones, mirroring the principles of computer network organization. The communication model has been compared to the [[Transmission Control Protocol]].<ref>B. Prabhakar, K. N. Dektar, D. M. Gordon, "The regulation of ant colony foraging activity without spatial information ", PLOS Computational Biology, 2012. URL: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002670</ref>

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2012年，Prabhakar和他的同事们发表了一项研究，内容涉及单个蚂蚁在没有信息素的情况下进行串联通信，这反映了计算机网络组织的原理。将通信模型与[[传输控制协议]]进行了比较

* 2016, first application to peptide sequence design.<ref name=":0" />

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2016年，首次应用于肽序列设计。

* 2017, successful integration of the multi-criteria decision-making method PROMETHEE into the ACO algorithm ([[HUMANT (HUManoid ANT) algorithm|HUMANT algorithm]]).<ref>{{cite journal|last1=Mladineo|first1=Marko|last2=Veza|first2=Ivica|last3=Gjeldum|first3=Nikola|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm|journal=International Journal of Production Research|date=2017|volume=55|issue=9|pages=2506–2521|doi=10.1080/00207543.2016.1234084|s2cid=114390939}}</ref>

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2017年，多准则决策方法PROMETHEE成功集成到ACO算法中

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==References==

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参考

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==Publications (selected)==

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