哪里可以了解更多关于“蚁群”的知识？ 优化？
我已经阅读了一段时间关于使用“蚁群”模型作为优化各种类型算法的启发式方法的内容。 然而，我还没有找到一篇文章或书籍以介绍性的方式甚至详细地讨论蚁群优化。 谁能给我指出一些资源，让我可以更多地了解这个想法？
I've been reading things here and there for a while now about using an "ant colony" model as a heuristic approach to optimizing various types of algorithms. However, I have yet to find an article or book that discusses ant colony optimizations in an introductory manner, or even in a lot of detail. Can anyone point me at some resources where I can learn more about this idea?
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如果你懂德语（是的，抱歉……），我和一个朋友写了一篇介绍关于这个主题的代码，我自己觉得还可以。 文本和代码以TSP为例来介绍这个概念。
即使如果您不懂德语，请看一下代码和文本中的公式，这可能仍然有用。
On the off chance that you know German (yes, sorry …), a friend and I have written an introduction with code about this subject which I myself find quite passable. The text and code uses the example of TSP to introduce the concept.
Even if you don't know German, take a look at the code and the formulas in the text, this might still serve.
链接维基百科实际上让我开始了。 我阅读了这篇文章并开始编码。 我正在解决旅行推销员问题的一个邪恶变体。 这是一个令人惊奇的元启发式方法。 基本上，可以放入图中的任何类型的搜索问题（节点和边，对称或不对称）都可以使用 ACO 来解决。
留意全球和本地信息素轨迹之间的差异。 本地信息素阻止一代蚂蚁走同一条路径。 它们阻止模型收敛。 全局信息素是吸引子，每代至少应该捕获一只蚂蚁。 他们鼓励几代人选择最佳路径。
我最好的建议就是简单地使用算法。 设置基本的 TSP 求解器和一些基本的菌落可视化。 然后玩得开心。 从概念上讲，与蚂蚁一起工作是非常酷的。 你对他们的基本行为进行编程，然后让他们放松。 我什至越来越喜欢他们了。 :)
ACO 是遗传算法的一种贪婪形式。 和他们一起玩。 改变他们的交流行为和群体行为。 您将很快开始以完全不同的方式看待网络/图形编程。 这是他们最大的好处，而不是大多数人所认为的秘诀。
你只需要用它来玩就能真正理解它。 书籍和 研究论文仅给出一般性的高水平理解。 就像自行车一样，你只需开始骑行即可。 :)
到目前为止，ACO 是我最喜欢的图问题抽象。
link Wikipedia actually got me started. I read the article and got to coding. I was solving a wicked variation of the traveling salesman problem. It's an amazing meta-heuristic. Basically, any type of search problem that can be put into a graph (nodes & edges, symmetric or not) can be solved with an ACO.
Look out for the difference between global and local pheromone trails. Local pheromones discourage one generation of ants from traversing the same path. They keep the model from converging. Global pheromones are attractors and should snag at least one ant per generation. They encourage optimum paths over several generations.
The best suggestion I have, is simply to play with the algorithm. Setup a basic TSP solver and some basic colony visualization. Then have some fun. Working with ants, conceptually, is way cool. You program their basic behaviors and then set them loose. I even grow fond of them. :)
ACOs are a greedier form of genetic algorithms. Play with them. Alter their communicative behaviors and pack behavior. You'll rapidly begin to see network / graph programming in an entirely different way. That's their biggest benefit, not the recipe that most people see it as.
You just gotta play with it to really understand it. Books & research papers only give a general sky-high understanding. Like a bike, you just gotta start riding. :)
ACOs, by far, are my favorite abstraction for graph problems.
《国家地理》不久前写了一篇有趣的文章，讨论了一些理论。
National Geographic wrote an interesting article awhile back talking about some of the theories.
有关这些主题的最佳资源是 Google 学术。 我研究蚁群优化算法有一段时间了，这里有一些不错的论文：
只是在谷歌学术上搜索“蚁群”。
另外，搜索 马可·多里戈。
The best resource for these topics is Google scholar. Ive been working on Ant Colony Optimization algorithms for a while, here are some good papers:
Just search for "Ant Colony" on google scholar.
Also, search for papers published by Marco Dorigo.
我很惊讶没有人提到 ACO 的圣经：
Marco Dorigo & Thomas Stützle: Ant Colony Optimization
这本书是ACO的作者写的，可读性很强。 您可以把它带到海滩并享受阅读的乐趣。 但它也是最完整的资源，非常适合在实施时作为参考。
您可以阅读 Google 图书上的摘录
智慧的体现是 ACO 主页
I am surprised nobody has mentioned the bible of ACO:
Marco Dorigo & Thomas Stützle: Ant Colony Optimization
This book is written by the author of ACO and it is highly readable. You can take it to the beach and have fun reading it. But it is also the most complete resource of all, great as a reference when implementing the thing.
You can read some excerpts on Google Books
Another great source of wisdom is the ACO Homepage
例如，请参阅学术百科上的这篇文章。
通过小世界图寻找路径的最有效方法是什么？ 问题。
See for example this article on scholarpedia.
There is also discussion here in the What is the most efficient way of finding a path through a small world graph? question.
乍一看，这似乎与Metropolis 算法密切相关（或者是其特殊情况）< /a>. 所以这是另一个可能的搜索方向。
补充： 此 PDF 文件 包括参考 1953 年最初的 Metropolis 论文。
At first glance this seems to be closely related to (or prehaps a special case of) the Metropolis algorithm. So that's another possible direction for searching.
Addition: This PDF file includes a reference to the original Metropolis paper from 1953.
好吧，我找到了 Eric Rollins 的主页 和他的不同实现（Haskell、Scala、Erlang、. ..) ACO 算法很有帮助。
还有 Enrique Alba 的书，题为“并行元启发式：一类新算法”，您可以在其中找到关于 ACO 算法及其不同用法的一整章解释。
热值
Well, i found the Homepage of Eric Rollins and his different Implementations (Haskell, Scala, Erlang,...) of a ACO Algorithm helpfull.
And also the Book from Enrique Alba, titled "Parallel Metaheuristics: A New Class of Algorithms" where you can find a whole chapter of explanation about ACO Algorithms and their different usages.
Hth