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第356行：
第356行： − It must visit each city exactly once;+ − + − − A distant city has less chance of being chosen (the visibility); 第368行：
第366行： − The more intense the pheromone trail laid out on an edge between two cities, the greater the probability that that edge will be chosen;+ − # It must visit each city exactly once;+ − 它必须访问每座城市一次 − Having completed its journey, the ant deposits more pheromones on all edges it traversed, if the journey is short; − # A distant city has less chance of being chosen (the visibility);+ − 远的城市只有很小的机会被选中（可视性） − After each iteration, trails of pheromones evaporate. − # The more intense the pheromone trail laid out on an edge between two cities, the greater the probability that that edge will be chosen; − 在两个城市之间的边上，信息素的踪迹越密集，被选中的可能性就越大 − # Having completed its journey, the ant deposits more pheromones on all edges it traversed, if the journey is short; − 蚂蚁完成了它的旅程，如果旅程很短的话，它会在经过的所有边缘上沉积更多的信息素 − center − − 中心 − − # After each iteration, trails of pheromones evaporate. − − 每次迭代后，信息素轨迹蒸发 第430行：
第413行： − + − + 第1,089行：
第1,072行： − +
→Applications
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.
它还被用于产生[[旅行商问题]]的近似最优解。在图形动态变化的情况下，蚁群算法具有类似问题的[[模拟退火]]和[[遗传算法]]方法的优势；蚁群算法可以连续运行，实时适应变化。这对[[网络路由]]和城市交通系统很有意义
它还被用于产生[[旅行商问题]]的近似最优解。在图形动态变化的情况下，蚁群算法具有类似问题的[[模拟退火]]和[[遗传算法]]方法的优势；蚁群算法可以连续运行，实时适应变化。这对[[网络路由]]和城市交通系统很有意义
:1.It must visit each city exactly once;
它必须每个城市只到达一次;
它必须每个城市只到达一次;
:2.A distant city has less chance of being chosen (the visibility);
较远的城市被选中的机会较少(可见度) ;
较远的城市被选中的机会较少(可见度) ;
The first ACO algorithm was called the ant system<ref name="Ant system">M. Dorigo, V. Maniezzo, et A. Colorni, ''[http://www.cs.unibo.it/babaoglu/courses/cas05-06/tutorials/Ant_Colony_Optimization.pdf Ant system: optimization by a colony of cooperating agents]'', IEEE Transactions on Systems, Man, and Cybernetics--Part B , volume 26, numéro 1, pages 29-41, 1996.</ref> and it was aimed to solve the travelling salesman problem, in which the goal is to find the shortest round-trip to link a series of cities. The general algorithm is relatively simple and based on a set of ants, each making one of the possible round-trips along the cities. At each stage, the ant chooses to move from one city to another according to some rules:
The first ACO algorithm was called the ant system<ref name="Ant system">M. Dorigo, V. Maniezzo, et A. Colorni, ''[http://www.cs.unibo.it/babaoglu/courses/cas05-06/tutorials/Ant_Colony_Optimization.pdf Ant system: optimization by a colony of cooperating agents]'', IEEE Transactions on Systems, Man, and Cybernetics--Part B , volume 26, numéro 1, pages 29-41, 1996.</ref> and it was aimed to solve the travelling salesman problem, in which the goal is to find the shortest round-trip to link a series of cities. The general algorithm is relatively simple and based on a set of ants, each making one of the possible round-trips along the cities. At each stage, the ant chooses to move from one city to another according to some rules:
:3.The more intense the pheromone trail laid out on an edge between two cities, the greater the probability that that edge will be chosen;
在两个城市之间的边上留下的信息素踪迹越强烈，这条边被选择的概率就越大;
在两个城市之间的边上留下的信息素踪迹越强烈，这条边被选择的概率就越大;
:4.Having completed its journey, the ant deposits more pheromones on all edges it traversed, if the journey is short;
旅程结束后，如果旅程很短，蚂蚁会在所经过的所有边上释放更多的信息素;
旅程结束后，如果旅程很短，蚂蚁会在所经过的所有边上释放更多的信息素;
:5.After each iteration, trails of pheromones evaporate.
每次迭代后，信息素踪迹会蒸发。
每次迭代后，信息素踪迹会蒸发。
[[File:Aco TSP.svg|thumb|600px|center]]
[[File:Aco TSP.svg|thumb|600px|center]]
具有序列依赖设置时间的单机总延迟问题 <ref>C. Gagné, W. L. Price and M. Gravel, "[https://link.springer.com/article/10.1057/palgrave.jors.2601390 Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times]," Journal of the Operational Research Society, vol.53, pp.895-906, 2002.</ref>
具有序列依赖设置时间的单机总延迟问题 <ref>C. Gagné, W. L. Price and M. Gravel, "[https://link.springer.com/article/10.1057/palgrave.jors.2601390 Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times]," Journal of the Operational Research Society, vol.53, pp.895-906, 2002.</ref>
*Multistage flowshop scheduling problem (MFSP) with sequence dependent setup/changeover times<ref>A. V. Donati, V. Darley, B. Ramachandran, "An Ant-Bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions", book chapter in Advances in Metaheuristics for Hard Optimization, Springer, {{ISBN|978-3-540-72959-4}}, pp.111-138, 2008.</ref>
*Multistage flowshop scheduling problem (MFSP) with sequence dependent setup/changeover times<ref>A. V. Donati, V. Darley, B. Ramachandran, "An Ant-Bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions", book chapter in Advances in Metaheuristics for Hard Optimization, Springer, pp.111-138, 2008.</ref>
具有序列依赖设置/转换时间的多级流车间调度问题 <ref>A. V. Donati, V. Darley, B. Ramachandran, "An Ant-Bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions", book chapter in Advances in Metaheuristics for Hard Optimization, Springer, {{ISBN|978-3-540-72959-4}}, pp.111-138, 2008.</ref>
具有序列依赖设置/转换时间的多级流车间调度问题 <ref>A. V. Donati, V. Darley, B. Ramachandran, "An Ant-Bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions", book chapter in Advances in Metaheuristics for Hard Optimization, Springer,, pp.111-138, 2008.</ref>
2016年，首次应用于肽序列设计。
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>
* 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>
2017年，多准则决策方法PROMETHEE成功集成到ACO算法中
2017年，多准则决策方法PROMETHEE成功集成到ACO算法中
==References==
==References==