Ant-Q Algorithm Based on Knowledge Transfer
Yuhu Cheng · Dianzi xuebao · 2011
The computational complexity of traditional Ant-Q algorithm shows factorial growth with the scale of the studied problem,which greatly reduces the convergence speed.Moreover,the traditional Ant-Q algorithm only focuses on a single task,therefore,the solution for the task cannot be reusable and the algorithm will handle a series of related tasks with low efficiency.In order to improve the convergence speed,a kind of Ant-Q algorithm based on knowledge transfer is proposed.At first,the similarity between each source task and a target task is computed according to the Bayesian theory.Then the obtained similarities are viewed as the weights to determine the number of samples transferred from every source task.In the third step,the samples from source tasks are listed in a descending order according to its transfer values and some valid samples are selected.In this way,the selected samples can guide an Agent to make a rational decision quickly.Simulation results involving a traveling salesman problem att532 illustrate that the knowledge transfer technology can effectively reduce the difficulty of learning a new task and quickly find an optimal solution.