Description: Ant colony algorithm is an Italian scholar
Dorigo et al. [1, 2], in the early 1990s, simulated the natural world
A population based inspiration for the behavior of the collective size of the ants
Bionic evolution system. Ant colony algorithm consists of two basic stages: adaptive order
Segment and collaboration phase. In the adaptive phase, the candidate solutions are based on accumulated information
Constantly adjust your structure. During the collaboration phase, the candidate solution passes information
Communication, in order to produce better solutions, is similar to learning automata
Learning mechanism. Ant colony algorithm is successfully used to solve the famous brigade
Business problem (t raveling salesman problem, TSP), this algorithm is adopted
The distributed positive feedback parallel computing mechanism is used to facilitate the junction with other methods
Combined with strong robustness [325].
Ant colony algorithm has been established for more than 10 years, whether in algorithm theory or computation
There have been many breakthroughs in the application of law.
- [antcolonyalgorithm.Zip] - an ant colony algorithm, we want to fine
- [Cscripteditor.Rar] - script editor, a very good tool, we can
- [ant-system-algorithm] - C language version of the ant system alg
- [OWT_SURELET] - Very block, the effect close to blsgsm.
- [HMMmodel] - This code implements in C++ a basic left
- [Tsp] - Java achieved with the use of ant colony
- [tsp1] - TSP Ant Colony Algorithm for a generic p
- [ant] - Ant colony algorithm with 2-opt technolo
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