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【简答题】Ant
colonies are successful because .A. all the ants know what’s best for the group B. the queen is a strong leader C. each ant does something useful
Ant colony optimization (ACO) is a probabalistic (stochastic), heuristic optimization technique inspired by the way ants make & find paths from the colony to food. The technique is used to solve discrete optimization problems that can be reduced to finding good paths through graphs. The first appearance of an ACO system was in a PhD thesis in 1992 by Marco Dorigo at Politecnico di Milano. It was called Ant System (AS). Since 1995 various other extended versions of AS have been developed, including Ant Colony System (ACS) and MAX-MIN Ant System (MMAS). In 1999 Dorigo proposed the ACO metaheuristic that became the most successful and recognized algorithm based on ant behavior.
B.
When one ant finds an optimal path from the colony to a food source, other ants are more likely to follow that path & positive feedback eventually causes all the ants to follow the same path. The ACO algorithm mimics this by walking around the graph representing the problem to solve. These algorithms have been applied to the symmetric & asymmetric traveling salesman problem with near-optimal results.
C.
There has been an interest in using ACO for network routing & urban transportation systems since the algorithm can be run continuously giving it the ability to adapt to changes in real time. This is an advantage over the simulated annealing & genetic algorithm approaches since they do not change dynamically.
D.
In machine learning & data mining problems, ACO variations have been used to create a model of the way worker ants "cluster" ant corpses in ant cemetary maintenance. This has been applied to a task called clustering in machine learning which involves finding groups of objects that are similar. This method has proven to have higher performance & accuracy than previous classical methods.