一种新的自适应蚁群算法及其应用

蚂蚁算法是一种新型的元启发式优化算法，初步的研究表明该算法具有较强的发现较好解的能力，但同时也存在一些缺点如容易出现停滞现象、收敛速度慢等。针对蚂蚁算法的不足，该文提出了一种自适应蚁群算法。该算法根据平均节点分支数动态地调整转移概率以避免算法出现停滞现象，从而极大地提高了算法搜索较好解的能力。仿真实验结果表明，新算法即使在运行的后期，仍然能以极大的概率搜索较好的解。

A Novel Adaptive Ant Colony Algorithm with Application

Ant algorithm is a novel meta-heuristic optimization algorithm. Preliminary study has shown that the algorithm has great ability of searching better solution, but at the same time there are some shortcomings such as tending to go into stagnation behavior and needing longer computing time. In order to overcome the shortcoming of basic ant algorithm, a new ant algorithm, Adaptive Ant Colony Algorithm (AACA), is proposed in this paper. In AACA, the transition probability with which ant used to select next city is dynamically adjusted based on the number of average node branch to avoid going into stagnation behavior. The ability of searching better solution is great improved in this way. Simulated experiments show that AACA has a good ability of searching better solution in the last runs of the algorithm.